Archive for carbon cycle – Page 4

A chalkier ocean? Multi-decadal increases in North Atlantic coccolithophore populations

Posted by mmaheigan 
· Saturday, November 19th, 2016 

Coccolithophores and the carbon cycle

Increasing atmospheric CO2 concentrations are resulting in both warmer sea surface temperatures due to the greenhouse effect and increasingly carbon-rich surface waters. The ocean has absorbed roughly one third of anthropogenic carbon emissions (1), causing a shift in carbon chemistry equilibrium to more acidic conditions with lower calcium carbonate saturation states (ocean acidification). Organisms that produce calcium carbonate structures are thought to be particularly susceptible to these changes (2-4).

Coccolithophores are the most abundant type of calcifying unicellular micro-algae in the ocean, producing microscopic calcium carbonate plates called coccoliths (5). Low-pH conditions have been shown to disrupt the formation of coccoliths (calcification; e.g., (6)). Therefore, it is generally expected that a higher-CO2 ocean will cause a reduction in calcification rates or a decrease in the abundance of these calcifiers. Such changes could have far-reaching consequences for marine ecosystems, as well as global carbon cycling and carbon export to the deep sea.

Coccolithophores use sunlight to synthesize both organic carbon through photosynthesis and particulate inorganic carbon (PIC) through calcification. Detrital coccolithophore shells form aggregates with organic material, enhancing carbon export to the deep sea (7). Coccolithophores also produce dimethyl sulfide (DMS), a climatically relevant trace gas that impacts cloud formation, ultimately influencing Earth’s albedo (8, 9). At the ecosystem level, coccolithophores compete for nutrients with other phytoplankton and provide energy for the rest of the marine food web. Coccolithophores have a broad range of irradiance, temperature, and salinity tolerances (10, 11). Moreover, their relatively low nutrient requirements and slow growth rates offer a competitive advantage under projected global warming and ocean stratification (5). This plasticity and opportunistic behavior can be critical for persistence in a changing oceanic environment. Given the wide range of biogeochemical and ecological processes impacted by coccolithophores, it is important to assess how anthropogenic changes may affect coccolithophore growth and calcification.

Many laboratory studies have investigated the impact of future environmental conditions on coccolithophores by decreasing pH, increasing dissolved inorganic carbon, and increasing temperature to mimic end-of-century projections. However, these have often yielded conflicting results: Some show a decrease, while others show no change or even increased calcification (e.g., (6, 12, 13)). For example, laboratory simulations of contemporary oceanic changes (increasing CO2 and decreasing pH) show that coccolithophores have the ability to modulate organic carbon production and calcification in response to variable amounts of dissolved inorganic carbon (DIC) but that low pH only affects these processes below a certain threshold (14). Another study indicated that coccolithophores could adapt to warming and highCO2 levels over the course of a year, maintaining their relative particulate organic carbon (POC) and PIC production per cell (15). One of the limitations of all laboratory experiments is that only a handful of species (and strains) are studied, which is only a tiny fraction of the diversity present in the oceans. Given the challenges of extrapolating laboratory results to real world oceans, studying recent trends in natural populations may lead to important insights.

The North Atlantic is both a region with rapid accumulation of anthropogenic CO2 (1) and an important coccolithophore habitat (Fig. 1), making this region a good starting point to search for in situ evidence of anthropogenic carbon effects on diverse coccolithophore populations. Two recent studies did precisely that: Rivero- Calle et al. (2015)(16) in the subpolar North Atlantic, and Krumhardt et al. (2016)(17) in the North Atlantic subtropical gyre. Using independent datasets, these two studies concluded that coccolithophores in the North Atlantic appear to be increasing in abundance and, contrary to the prevailing paradigm, responding positively to the extra carbon in the upper mixed layer.

Evidence from long-term in situ monitoring (two independent case studies)

Rivero-Calle et al. (2015) used data from the Continuous Plankton Recorder (CPR), a filtering device installed on ships of opportunity, to assess changes in coccolithophore populations from 1965 to 2010 in the subpolar North Atlantic. This highly productive, temperate region is dominated by large phytoplankton and characterized by strong seasonal changes in the mixed layer depth, nutrient upwelling, and gas exchange that lead to intense, well-established spring phytoplankton blooms.

Because coccolithophore cells are smaller than the mesh size used by the CPR, they cannot be accurately quantified in the CPR data set. Some coccolithophore cells do, however, get caught in the mesh and their occurrence (i.e. probability of presence) can be calculated and serve as a proxy for coccolithophore abundance. Using recorded presence or absence of coccolithophores over this multidecadal time-series, the authors showed that coccolithophore occurrence in the subpolar North Atlantic increased from being present in only 1% of samples to > 20% over the past five decades (Fig. 2). To assess the importance of a wide range of diverse environmental drivers on changes in coccolithophore occurrence, Rivero-Calle and co-authors used random forest statistical models. Specifically, they examined more than 20 possible biological and physical predictors, including CO2 concentrations, nutrients, sea surface temperature and the Atlantic Multidecadal Oscillation (AMO), as well as possible predators and competitors. Global and local CO2 concentrations were shown to be the best predictors of coccolithophore occurrence. The AMO, which has been in a positive phase since the mid-1990s (Fig. 2) and is associated with anomalously warmer temperatures over the North Atlantic, was also a good predictor of coccolithophore occurrence, but not as strong of a predictor as CO2.

The authors hypothesize that the synergistic effects of increasing anthropogenic CO2, the recent positive phase of the AMO, and increasing global temperatures contributed to the observed increase in coccolithophore occurrence in the CPR samples from 1965 to 2010. Complementing the Rivero-Calle et al. (2015) study, Krumhardt et al. (2016) used phytoplankton pigment concentration data from the long-running Bermuda Atlantic Time-series Study (BATS) and satellite-derived PIC data to assess recent changes in coccolithophore abundance in the subtropical North Atlantic. This region of the North Atlantic is characterized by Ekman convergence and downwelling, resulting in an oligotrophic environment. Despite relatively low productivity, subtropical gyres cover vast expanses of the global ocean and are thus important on a global scale.

In the North Atlantic subtropical gyre, researchers at BATS have performed phytoplankton pigment analyses since the late 1980s, as well as a suite of other oceanographic measurements (nutrients, temperature, salinity, etc.). This rich dataset provided insight into phytoplankton dynamics occurring at BATS over the past two decades. Coccolithophores contain a suite of pigments distinctive to haptophytes. Though there are many species of non-calcifying haptophytes in the ocean (18), the main contributors to the haptophyte community in oligotrophic gyres are coccolithophores (19). Using a constant haptophyte pigment to chlorophyll a ratio Krumhardt et al. (2016) quantified relative abundance of the coccolithophore chlorophyll a (Chlahapto) over the BATS time-series. A simple linear regression revealed that coccolithophore pigments have increased in the upper euphotic zone by 37% from 1990 to 2012 (Figure 2). On the other hand, total chlorophyll a at BATS only increased slightly over this time period.

While satellite-derived chlorophyll a is used as a proxy for biomass and abundance of the entire phytoplankton community (20), satellite-derived PIC is formulated to specifically retrieve calcium carbonate from coccolithophore shells (21, 22). Therefore, satellite PIC can be used as a proxy for coccolithophore abundance. Although there has been virtually no change in total chlorophyll a over most of the North Atlantic subtropical gyre over the satellite era (1998-2014), predominantly positive trends were shown over this time period for PIC (17). This indicates that coccolithophore populations appear to be increasing over and above other phytoplankton species in the subtropical gyre.

Like Rivero-Calle et al., Krumhardt et al. explored possible environmental drivers of this increase in coccolithophore pigments at BATS and coccolithophore PIC throughout the gyre. They performed linear correlations between variability of hypothesized drivers and coccolithophore chlorophyll a concentrations at the BATS site. Increasing DIC, specifically the bicarbonate ion (HCO3–) fraction, showed a strong positive correlation with pigments from coccolithophores, explaining a significant fraction of the coccolithophore pigment variability. DIC in the upper mixed layer at BATS has been increasing steadily over the past several decades from absorption of anthropogenic CO2 (Fig. 2; 23) and coccolithophores may be responding to this. But how does extra carbon in the water explain the increases in coccolithophore populations?

Environmental controls on coccolithophore growth

A few studies have shown that, in contrast to most other phytoplankton, coccolithophore photosynthesis (specifically, the widespread coccolithophore species Emiliania huxleyi) can be carbon-limited at today’s CO2 levels (e.g., 14, 24). This suggests that increases in surface DIC (e.g., due to the uptake of anthropogenic CO2) may alleviate growth limitation of coccolithophores. By reducing the amount of energy spent on carbon concentrating mechanisms, coccolithophores may invest in other metabolic processes such as growth, PIC or POC production. This explains why a relatively small increase in DIC could increase coccolithophore competitive ability, especially in oligotrophic environments where phytoplankton are routinely in competition for scarce nutrients. Rivero-Calle and co-authors compiled numerous published laboratory studies that assessed coccolithophore growth rates as a function of pCO2. The compilation, which included several species and strains of coccolithophores, showed that there is a quasi-hyperbolic increase in coccolithophore growth rates as pCO2 increases (Fig. 3). The range of local pCO2 concentrations in the subpolar/temperate North Atlantic from 1965 to 2010 (~175 to 435 ppm) spanned the pCO2 levels over which there is a substantial increase in published coccolithophore growth rates (Fig. 3). Growth rates tend to stabilize at ~500 ppmCO2, indicating that coccolithophore populations may continue to respond positively to increasing CO2 for the next few decades.

Other environmental factors (e.g., temperature, light, and available nutrients) may also impact and modulate coccolithophore growth rates, resulting in a net neutral or net negative impact in spite of increasing atmospheric (marine) CO2 (DIC) concentrations (see conceptual model, Fig. 3). For example, severe nutrient limitation in the subtropics may cause coccolithophores to be outcompeted by smaller marine cyanobacteria. In the subpolar North Atlantic, nutrients are more plentiful than in the subtropics, but Earth system models have predicted that climatic warming in this region may result in increased water column stratification (25). Under these stratified low-nutrient conditions, smaller phytoplankton such as coccolithophores could become more prevalent at the expense of larger phytoplankton such as diatoms (26, 27). However, if nutrient concentrations decline to the point at which they become the limiting factor for growth, then coccolithophore populations will also be negatively affected. Furthermore, the associated drop in pH from CO2 dissolving into the upper mixed layer can eventually be detrimental to coccolithophore growth and calcification. Specifically, pH values below 7.7 negatively affected the coccolithophore Emiliania huxleyi in laboratory experiments (14), though most oceanic regions will not show such a low pH any time in the near future. In short, anthropogenic CO2 entering the ocean may allow coccolithophores a competitive edge in the near future in some regions such as the North Atlantic, but other compounding influences from anthropogenic climate change such as severe nutrient limitation or ocean acidification are also important to consider, particularly in the oligotrophic gyres.

Open questions and future directions

While recent work has provided new insight into the impact of several environmental factors (irradiance, nutrients, temperature, pH, DIC) on coccolithophores, many questions remain. Among these, the vertical distribution of coccolithophore communities, grazing rates, and viral infection on coccolithophores, and species-specific responses to environmental change are relatively unexplored areas of research. For instance, some studies have shown species-specific and even strain-specific variability in the response of coccolithophores to CO2 (28, 29), but how various coccolithophore species respond to nutrient or light limitation is relatively unknown. Due to its cosmopolitan distribution and ability to grow relatively easily in the lab, E. huxleyi, has become the “lab rat” species. However, it may not be the most important calcite producer globally (30), nor the most representative of the coccolithophore group as a whole. As part of its peculiarities, E. huxleyi can both produce several layers of coccoliths and also exhibit a naked form without coccoliths, posing questions about the importance of non-calcified forms in the projected acidified oceans and about the role of calcification per se (31). Indeed, the fundamental question of why coccolithophores calcify is still unresolved and may vary between species (5, 32). In addition, while we recognize that some zooplankton groups graze on coccolithophores (coccoliths have been found in pelagic tintinnid ciliates (33), as well as copepod guts and fecal pellets (34-36)), little is known about predation rates or specificity in natural populations. Finally, we know that viruses can also cause bloom termination and that E. huxleyi can induce coccolith detachment to avoid viral invasion (37); however, there are still many unknowns related to bloom dynamics. Until we understand what drives coccolithophore calcification and variations in growth and mortality rates, we will have an incomplete picture of the role that coccolithophores play in marine ecology and the carbon cycle.

Krumhardt et al (2016) and Rivero-Calle et al (2016) both arrive at a simple conclusion: Coccolithophore presence in the North Atlantic is increasing. The common denominators in this equation are increasing global CO2 levels and increasing global surface temperatures. Therefore, even given regional oceanic variability in environmental drivers, we might expect to see similar trends in coccolithophore abundance in other regions. Given coccolithophores’ positive response to increasing anthropogenic CO2 and temperature, as well as general fitness under conditions that may be more prevalent in the future ocean, coccolithophores may become an even bigger player in the marine carbon cycle, which may have unexpected consequences.

Authors
Kristen Krumhardt (University of Colorado Boulder)
Sara Rivero-Calle (University of Southern California, Los Angeles)
Acknowledgments
We would like to thank co-authors on the Rivero-Calle et al. (2015) and Krumhardt et al. (2016) studies for their contributions to the research described. We also would like to thank Nikki Lovenduski and Naomi Levine for helpful comments in composing this OCB Newsletter piece. Many thanks to APL, NSF, NOAA and NASA for funding, and SAHFOS, ICOADS, the BATS research group, and NASA for their long-term data and making it freely available.

References

1. C. L. Sabine et al., Science 305 (2004).
2. Feely et al., Science 305, 362-366 (2004).
3. J. C. Orr et al., Nature 437, 681-686 (2005).
4. K. J. Kroeker et al., Global Change Biology 19, 1884-1896 (2013).
5. B. Rost, U. Riebesell, In Coccolithophores: from Molecular Processes to Global Impact, 99-125 (2004).
6. U. Riebesell et al., Nature 407, 364-367 (2000).
7. C. Klaas, D. E. Archer, Glob.Biogeochem. Cycles 16, (2002).
8. M. D. Keller, W. K. Bellows, R. R. L. Guillard, Acs Symposium Series 393, 167-182 (1989).
9. S. M. Vallina, R. Simo, Science 315, 506-508 (2007).
10. T. Tyrrell, A. Merico, In H. R. Thierstein, J. R. Young, Eds.,Coccolithophores: from Molecular Processes to Global Impact (2004),pp. 75-97.
11. W. M. Balch, In Coccolithophores: from Molecular Processes to Global Impact, 165-190 (2004).
12. M. D. Iglesias-Rodriguez et al., Science 320, 336-340 (2008).
13. S. Sett et al., Plos One 9, (2014).
14. L. T. Bach et al., New Phytol. 199, 121-134 (2013).
15. L. Schluter et al., Nature Clim. Change 4, 1024-1030 (2014).
16. S. Rivero-Calle, A. Gnanadesikan, C. E. Del Castillo, W. M.Balch, S. D. Guikema, Science 350, 1533-1537 (2015).
17. K. M. Krumhardt, N. S. Lovenduski, N. M. Freeman, N. R.Bates, Biogeosci. 13, 1163-1177 (2016).
18. H. Liu et al., Proc. Nat. Acad. Sci. 106, 12803-12808 (2009).
19. Y. Dandonneau, Y. Montel, J. Blanchot, J. Giraudeau, J. Neveux, Deep-Sea Res. Part I-Oceanographic Research Papers 53, 689-712(2006).
20. M. J. Behrenfeld, P. G. Falkowski, Limnol. Oceanogr. 42, 1-20(1997).
21. H. R. Gordon et al. (Geochemical Research Letters, 2001), vol.28, pp. 1587-1590.
22. W. M. Balch, H. R. Gordon, B. C. Bowler, D. T. Drapeau, E. S.
Booth, J. Geophys. Res.Oceans 110 (2005).
23. N. R. Bates et al., Oceanogr. 27, 126-141 (2014).
24. B. Rost, U. Riebesell, S. Burkhardt, D. Sultemeyer, Limnol.Oceanogr. 48, 55-67 (2003).
25. A. Cabr., I. Marinov, S. Leung, Clim.Dyn. 1-28 (2014).
26. L. Bopp, O. Aumont, P. Cadule, S. Alvain, M. Gehlen, Geophys.Res. Lett. 32, (2005).
27. I. Marinov, S. C. Doney, I. D. Lima, Biogeosci. 7, 3941-3959 (2010).
28. Langer et al., Geochem. Geophys. Geosys. 7, (2006).
29. Langer, G. Nehrke, I. Probert, J. Ly, P. Ziveri, Biogeosci. 6, 2637-2646 (2009).
30. C. J. Daniels et al., Marine Ecol. Prog. Ser. 555, 29-47 (2016).
31. M. N. Muller, T. W. Trull, G. M. Hallegraeff, Marine Ecol. Prog.Ser. 531, 81-90 (2015).
32. F. M. Monteiro et al., Science Advances 2, (2016).
33. J. Henjes, P. Assmy, Protist 159, 239-250 (2008).
34. S. Honjo, M. R. Roman, J. Marine Res. 36, 45-57 (1978).
35. R. P. Harris, Marine Biol. 119, 431-439 (1994).
36. J. D. Milliman et al., Deep Sea Res. Part I: Oceanographic Research Papers 46, 1653-1669 (1999).
37. M. Frada, I. Probert, M. J. Allen, W. H. Wilson, C. de Vargas, Proc. Nat. Acad. Sci. 105, 15944-15949 (2008).

What controls the distribution of dissolved organic carbon in the surface ocean?

Posted by mmaheigan 
· Friday, November 11th, 2016 

Around 662 billion tons of organic carbon are dissolved in the ocean, making the pool one of Earth’s major, exchangeable carbon reservoirs. Dissolved organic carbon (DOC) has many ecological functions. It can form complexes with metals (1); absorb UV and visible light, acting as a “sunscreen” for marine microorganisms and controlling primary production in the upper water column (2); it has antioxidant activity, reacting with free radicals in the media (3); but most importantly, it serves as substrate for the microbial loop and as a vehicle for carbon sequestration in the ocean. Therefore, DOC plays an important role in climate on geological time scales.

Because the amount of atmospheric CO2 is of the same magnitude as the DOC pool, and is closely linked to it through exchange, variations in one of these reservoirs can affect the other, impacting the carbon cycle with consequences for climate. Significant net DOC remineralization would lead to an increase of atmospheric CO2, enhancing greenhouse warming at the surface of the Earth. Net oxidation of only 1% of the seawater DOC pool within 1 year would be sufficient to generate a CO2 flux of 7 PgC/yr, comparable to that produced annually by fossil fuel combustion (4). It has also been proposed that a large-scale oxidation of DOC may have prevented a dramatic global glaciation (‘snowball earth’) in the Neoproterozoic period (5).

Despite its importance, knowledge about DOC dynamics is relatively limited; in fact, it was considered highly inert until about three decades ago when a new analytical technique for measuring it via high-temperature catalytic oxidation stimulated new interest (6). The technique eventually provided more accurate DOC values, showing that it was more involved in the carbon cycle than previously thought and that its concentrations vary with depth, time, and location. Considering DOC distributions observed in the surface Atlantic Ocean (Fig. 1), we see values in the subtropical gyres of 65-70 µmol Kg-1, the highest concentrations in the tropics (> 70 µmol Kg-1), the lowest in the Southern Ocean (< 50 µmol Kg-1), and moderate concentrations in the northern North Atlantic (55-60 µmol Kg-1); this pattern is consistent in other ocean basins. So what controls this distribution and can we predict it? Even with improved analytical techniques, DOC is not a variable that can be measured easily at sea, and the sampling must be done carefully since it is easy to contaminate. Therefore, DOC data are typically fewer than those of other more readily determined variables such as nutrients and oxygen. If we could predict DOC from variables for which much greater global ocean coverage exists, we could fill the very large spatial and temporal gaps in the DOC fields.

DOC is produced in the upper water column by phytoplankton (primary producers). Actually, half of the inorganic carbon that is fixed by phytoplankton is transformed to DOC. Heterotrophic microbes consume most of that DOC, but ~ 4% of global annual net primary production (~ 2 Pg C y-1) (7) accumulates as DOC, much of which is exported to the mesopelagic via vertical mixing and convergence, thus contributing to the biological carbon pump.

New primary production, the foundation of a system’s net community production (NCP), depends on new nutrients reaching the euphotic zone, which happens primarily via upwelling in divergence zones and winter vertical mixing. NCP is the balance of the carbon generated by primary producers minus that lost through heterotrophic respiration (prokaryotes and animals). It can be estimated either by a loss of reactants (CO2 or nutrients) or a gain in products (suspended POC, DOC, and export production) (8).

In our work, we needed to establish the fraction of NCP that was present in dissolved form (i.e., the net DOC production ratio, or NDPr). For that, we simply estimated NCP from the nitrate (NO3–) that is consumed in the euphotic zone (DNO3–):

ΔNO3– = new NO3– (introduced from deeper layers) – remaining NO3– (at surface) (Eq. 1)

In the same way, we also calculated net accumulated DOC, or ΔDOC:

ΔDOC = DOC in euphotic zone – DOC introduced from deeper layers (Eq. 2)

The ratio between ΔDOC and ΔNO3– gave us the NDPr:

NDPr = ΔDOC/ΔNO3– (Eq. 3)

NDPr was calculated throughout the Atlantic Ocean using observations of DOC and NO3– from >15 international oceanographic cruises over the last decade, including those occupied by the US Repeat Hydrography program (Fig. 1). Values of NDPr mostly varied between 0.1 and 0.4 (Fig. 2), with the exception of the North Atlantic Subtropical Gyre (NASG), where NDPr values reach >0.8 at times. After sensitivity testing, we applied a NDPr value of 0.17 to the entire basin, which yielded the smallest error between calculated and observed DOC concentrations. Applying this NDPr value to ΔNO3– (i.e. NCP) obtained from cruise data, we estimate ΔDOC (Eq. 4), in which 6.6 is the molar conversion from N to C units:

ΔDOC= ΔNO3– * 6.6 * 0.17 = NCP * 0.17 (Eq. 4)

To obtain the calculated DOC concentration (DOCcalculated), we added the DOC concentration of underlying source waters (DOCsource) to ΔDOC (Eq. 5):

DOCcalculated = DOCsource + ΔDOC (Eq. 5)

When comparing calculated vs. observed DOC (Fig. 3), we found significant agreement (R2 = 0.64; p < 0.001; n=268) throughout the Atlantic, except in the western North Atlantic, where observed DOC > estimated DOC, especially in the southern sector. After this validation of our approach using nutrients and DOC observations, we applied the method to the more extensive NO3– distributions available in the World Atlas Ocean (WOA) climatology to develop a DOCcalculated map for the entire Atlantic (Fig. 4a). The calculated values agree well with the observations, with a total error of 8.94%.

How much DOC is annually produced in the surface Atlantic Ocean? Total organic carbon export (considered equivalent to NCP) in the Atlantic has been estimated to be 4.15-4.3 Pg C y-1 (9, 10). Applying the 0.17 NDPr (equation 3) indicates that 0.70-0.75 Pg C y-1 accumulates in the Atlantic surface as DOC; as such, the Atlantic accounts for ~36% of the global net DOC production ~2 Pg C y-1.

In permanently stratified areas like the southern sectors of the NASG, our approach is invalid since there is little nutrient input from underlying depths. Also, the static view of our approach does not take into account advection that will modify the DOC distributions, nor does it account for eventual removal of accumulated and advected DOC by microbes. To account for these influences on distributions, we applied the ΔNO3– measurements to a steady-state ocean circulation model including terrestrial DOC inputs and DOC remineralization (Fig. 4b). In the model, zonal advection is evident through enrichment of DOC in the Caribbean Sea. Also, inputs of terrestrial DOC are observed near the outflow of the Amazon River. However, the model only slightly improved the match between observations and modeled DOC, with a total error of 8.71% vs. the 8.94% obtained before the model application.

The correspondence between observations and modeled values was good, considering that we are comparing observations of DOC from cruises during specific seasons with estimates based on more idealized nutrient climatology. The main mismatch is found in the western NASG, where observations can reach 13 µmol Kg-1 higher than calculated values. Local production and/or allochthonous inputs of either new nutrients or DOC must be considered. Local production of DOC could result from addition of nitrogen from sources beyond vertical mixing such as diazotrophic N2 fixation, atmospheric deposition, and river runoff. Alternatively, DOC can be concentrated by evaporation, as is sea salt. However, none of these explain the high DOC values observed in the NASG. DOC flux estimated from dissolved organic nitrogen (DON) released by N2 fixation (11) is too low to explain the extra DOC. Regarding the atmospheric deposition, aerosol optical depth data suggest higher deposition in the eastern than in the western North Atlantic (11), and no excess of DOC is observed there. According to salinity distributions from the World Ocean Atlas, advection of DOC from the closest major rivers (Amazon and Orinoco) does not extend far enough northward to explain the NASG anomaly. Salinity normalization of DOC does not erase the feature, indicating that evaporation is not the cause. Those elevated values of carbon are found during cruises from 2003 in the same area (12), so it appears to be a persistent feature. The anomaly also coincides with a DON maximum and a light stable isotope (δ15N) composition in the particulate organic carbon based on measurements recorded in 2004 (13). An explanation for these anomalies has not been confirmed.

 

Conclusions

New nutrients are the fundamental driver of net DOC accumulation in the surface Atlantic Ocean. As such, climate-driven changes in ocean dynamics, which will affect the supply of nutrients to the euphotic zone, will affect the DOC inventory. The effects of climate change on the nutrient supply to the upper water column are not well known, but they will depend on the opposing influences of thermal stratification and upwelling intensification. Some authors predict an intensification and spatial homogenization of coastal upwelling systems (14, 15). Such would increase the nutrient input to the euphotic zone and the net DOC production. In contrast, others have reported that ocean warming should intensify thermal stratification, reducing nutrient flux by vertical mixing in regions not affected by coastal upwelling systems (16, 17). Depending on which of these phenomena dominate, the nutrient supply will change, in turn changing the DOC budget and its distribution. Furthermore, the percentage of NCP accumulating as DOC (i.e. NDPr), found here to be ~17%, could change in response to a shift in the balance of autotrophs and heterotrophs. This multitude of influencing factors will undoubtedly impact the future course of the oceanic DOC budget.

 

Authors

Cristina Romera-Castillo (Univ. of Vienna) and Dennis A. Hansell (RSMAS, Univ. Miami)

Acknowledgments

The authors thank the other co-author, Robert T. Letscher, from the more extended version of this published work. Also to Dr. X.A. Álvarez-Salgado for the use of DOC data he collected during cruises supported by the Spanish government. Data collection on US CLIVAR sections and involvement by C.R.-C. and D.A.H. were supported by US National Science Foundation OCE1436748.

References

  1. Midorikawa, T., E. Tanoue, 1998. Mar. Chem. 62, 219-239.
  2. Arrigo, K. R., C. W., Brown, 1996. Mar. Ecol. Prog. Ser. 140, 207-216.
  3. Romera-Castillo, C., R. Jaffé, 2015. Mar. Chem. 177, 668–676.
  4. Hedges, J. I. 2002. In: Hansell, D., Carlson, C. (Eds.), 2002. Biogeochemistry of marine dissolved organic matter. Academic Press, San Diego, pp. 1-33.
  5. Peltier, W. R. et al., 2007. Nature 450, 813-819.
  6. Hansell, D. A., C. A. Carlson, 2015. Eos, 96, doi:10.1029/2015EO033011.
  7. Hansell, D. A., et al., 2009. Oceanography 22, 202-211.
  8. Hansell, D. A., C. A. Carlson, 1998. Global Biogeochem. Cycles 12, 443-453.
  9. Laws, E. A., et al., 2000. Global Biogeochem. Cycles 14, 1231-1246.
  10. Dunne, J. P., et al., 2007. Global Biogeochem. Cycles 21, GB4006.
  11. Benavides, M., et al., 2013. J. Geophys. Res.: Oceans 118, 3406-3415.
  12. Carlson, C. A. et al., 2010. Deep-Sea Res. Pt II 57, 1433-1445.
  13. Landolfi, A. et al., 2016. Deep-Sea Res. Part I 111, 50-60.
  14. Sydeman, W. J. et al., 2014. Science 345, 77-80.
  15. Wang, D. et al., 2015. Nature 518, 390-394.
  16. Cermeño, P. et al., 2008. PNAS 105, 20344-20349.
  17. Bopp L, et al., 2013. Biogeosciences 10, 6225-6245.
  18. Schlitzer, R., 2015. Ocean Data View. Available at https://odv.awi.de
  19. Romera-Castillo, C. et al., 2016. PNAS 113, 10497–10502.

Marine particles: Distribution, composition, and role in scavenging of TEIs

Posted by mmaheigan 
· Sunday, July 3rd, 2016 

GEOTRACES and particles in the ocean

GEOTRACES is an international program to study the global marine biogeochemical cycles of trace elements and their isotopes (TEIs). The program’s guiding mission is to “identify processes and quantify fluxes that control the distributions of key TEIs in the ocean” (1).

Particles represent a key parameter for the GEOTRACES program because of their role as sources, sinks, and in the internal cycling of so many TEIs (1, 2). Particles in the ocean fall into two classes: 1. Those that have sources external to the system such as lithogenic material carried by atmospheric transport, river, or lateral transport from continental margin sediments; and 2. those that are produced internally in the system, primarily by biological production, but also by authigenic mineral precipitation (2).

External particle sources such as mineral dust deposition and sediment resuspension act as sources of dissolved TEIs when they partially dissolve in seawater. Conversely, dissolved TEIs are removed by active biological uptake or passive adsorption onto particles surfaces, followed by particle removal by aggregation and sinking. Indeed, the biological and abiotic interactions of dissolved TEIs with particles determine the residence time of a dissolved TEI.

In most open ocean basins away from ocean floor boundaries, external sources of particles are dwarfed by the much greater biological production and destruction of particles. Particle cycling in most open ocean basins is thus dominated by the biological pump, the processes by which suspended particles are produced by photosynthesis in the euphotic zone at the surface, and are then abiotically or biologically aggregated into larger particles that can sink into the abyss (3).

As particulate organic carbon (POC) cycles through processes such as aggregation, disaggregation, remineralization, and sinking (collectively referred to here as particle dynamics), other particle phases are swept along for the ride, including other major components such as biologically precipitated minerals (especially CaCO3 and opal), as well as lithogenic and authigenic particles, and scavenged TEIs adsorbed to the surfaces of other particles (Fig. 1).

In this article, I will briefly review the role of particle composition on the scavenging of TEIs.

Scavenging: A two-step removal process

Most adsorption of TEIs likely occurs onto small, suspended particles, which are usually more abundant, have more available surface area, and have a longer residence time in the water column than large, sinking particles. For TEIs to be removed from the water column, the suspended particles must then be aggregated into larger, sinking particles. There are thus two distinct steps for the removal of a dissolved TEI by scavenging: 1) adsorption onto suspended particle surfaces, followed by 2) removal via the aggregation of suspended particles onto larger particles that sink out of the water column. Fig. 1 shows a very simple schematic illustrating these basic processes. The adsorption step is governed by the affinity of a TEI for a particular particle surface, and the removal step is governed by the particle dynamics that package suspended particles into large, sinking aggregates, and are the focus of studies of the biological pump. The removal of TEIs by scavenging thus intimately links one of OCB’s scientific goals, the understanding of the biological carbon pump, to GEOTRACES’s mission to identify processes and quantify fluxes that control the distributions of key TEIs in the ocean.

Particle concentration and composition: horizontal and vertical variations

Particles collected in the ocean are a heterogeneous mixture of biogenic, lithogenic, and authigenic (precipitated in-situ) components. The relative proportions of these different components vary geographically and with depth. Fig. 2 shows the distribution of total particle concentration from GA03, the U.S. GEOTRACES North Atlantic Zonal Transect cruise in 2010/2011, as well as the changing composition of small (<51 mm) particles at three stations along the transect (4). Particle concentrations are highest at the surface and at the margins, where biological production is highest. It is clear that particulate organic matter (POM) dominates particle composition in the upper 100 m, making up more than 70% of the suspended particle mass at all three stations. The balance in the upper 100 m is mostly made of other biogenic components such as CaCO3 and opal, with a small contribution from lithogenic particles directly under the Saharan dust  plume. At all stations, the inorganic components (everything except for POM) become relatively more important with depth as POM is remineralized. In the eastern half of the basin, lithogenic particles make up the largest fraction of particle mass, accounting for >50% of particle mass below 1500 m. In the western half of the basin, further from the Saharan dust source, lithogenic particles are not as important, and CaCO3 makes up the largest fraction (~50%) of particle mass between 500 – 3000 m. A special case is found in a station over the Mid-Atlantic Ridge, where iron oxyhydroxides from the hydrothermal plume make up ~50% of the particle mass. Iron and manganese oxyhydroxides are rarely dominant components of particle mass, except in special situations such as hydrothermal plumes, but may exert a particularly large influence on TEI adsorption (5, 6 ).

Studies suggest that particle composition may affect both the affinity of dissolved TEIs for adsorbing onto particle surfaces (2), and the vertical flux of particles from the water column (7-9). Horizontal and vertical changes in particle composition thus allow us to test hypotheses of the importance of particle composition on both steps in the scavenging of TEIs.

Effect of particle composition on adsorption of TEIs

The affinity of TEIs to particles has typically been characterized by a partition coefficient, Kd, which is calculated empirically as:

Prior to the GEOTRACES program, the effect of particle composition on TEI adsorption affinity had been studied in the field using sediments and sinking particles collected in sediment traps. The affinity of trace metals to marine sediments of different compositions varied: Some trace metals (Cs, Be, Sn, and Fe) had a higher affinity to sediments dominated by aluminosilicate clay minerals, and others (Ba, Cd, Zn, Mn, and Co) had a higher affinity to sediments enriched in Mn oxyhydroxides (10). In the water column, correlations between the partition coefficient of 230Th and particle composition in sediment trap particles from around the world have variously implied that the scavenging efficiency of 230Th is controlled by CaCO3 (11, 12), lithogenic material (13, 14), and/or Mn oxyhydroxides (15). Studies that span strong opal gradients across the Polar Front in the Southern Ocean show higher partition coefficients for 231Pa scavenging in areas of high opal content (11, 16). 231Pa is generally not as particle-reactive as 230Th in the open ocean, but is often removed with equal efficiency as 230Th in near-margin areas (e.g., 17), presumably because opal is more important in margin settings. The Arctic, on the other hand, displays the opposite 230Th/231Pa removal signal, with 231Pa removal less efficient relative to 230Th at the margins compared to the open ocean (18).

Since TEIs adsorb primarily onto suspended particles rather than sinking particles, studying the correlations between partition coefficients and suspended particles may resolve some of the discrepancies observed in the sediment trap studies (c.f., 2).

The GEOTRACES GA03 North Atlantic Zonal Transect has provided the first opportunity to investigate the correlation between partition coefficients of various TEIs and the particle composition of suspended particles in the ocean. Thus far, this has been done for 230Th and 231Pa partition coefficients, with Mn and Fe oxyhydroxides emerging as key controlling phases and opal having no controlling effect (5). The North Atlantic is very opal-poor (Fig. 2), so particles collected from more diatom-rich regions are needed to examine the potential of opal as a controlling phase. Other studies are underway to study the  particle affinities of Hg (19), Po (20), and Pb (6) on this same North Atlantic transect. Subsequent U.S. GEOTRACES sections (GP16—Eastern Tropical South Pacific Zonal Transect and GN01—Western Arctic) will also have full ocean depth size-fractionated particle concentration and composition, allowing us to examine samples from different biogeochemical provinces, and hopefully expanding the range of particle compositions.

TEIs as tracers of scavenging rates and particle dynamics

The unprecedented data sets from GEOTRACES are also allowing us to estimate adsorption and desorption rate constants (Fig. 1) from inverse modeling of the observations of dissolved and particulate TEIs and particle concentrations (19, 21, 22). This gives us a kinetic view of the scavenging process to complement the empirically-derived partition coefficients, which are often viewed as representing equilibrium constants. Applying inverse modeling approaches to observations of the distributions of size-fractionated particles and particulate TEIs can also allow us to estimate rates of particle remineralization, aggregation, disaggregation, and sinking (21, 23). This approach requires only that a conceptual model relating the suspended and sinking particle size fractions be applied to observations of particle mass and particulate TEIs, and does not require knowledge about which specific physical or biological processes are responsible for particle transformations. For example, Fig. 1 illustrates a simple conceptual model in which a pool of suspended particles can be lost to the dissolved phase through remineralization, or by aggregation into sinking particles; conversely, sinking particles can sink, or can be disaggregated back into suspended particles. By assuming that particulate TEIs are simply part of the overall particle pool (e.g., a coating on organic particles in the case of radiogenic TEIs such as 230Th or as part of a lithogenic particle in the case of a TEI such as Ti) and thus are subject to the same rates of particle transformations as the major phases such as POC, we can apply the same conceptual model to observations of particle mass and to observations of particulate TEI to better constrain the rates of these transformations (23). As some of these rates such as aggregation and disaggregation are notoriously difficult to measure directly, these inverse approaches offer a way forward to quantify these important processes.

Particle composition and the biological pump

In addition to its effect on scavenging efficiency, particle composition has also been implicated as an important factor in the strength and efficiency of the biological pump. Several meta-analyses of global deep (>1000 m) sediment trap data showed strong correlations between POC flux and mineral flux (7-9), leading to the development of the “ballast hypothesis.” The mechanisms to explain the correlations, which are still being debated (24, 25), range from mineral protection of POC (8), mineral contribution to particle excess density (7), scavenging of mineral particles by POC (26), and minerals as proxies for particle packaging, POC lability, and ecosystem structure (9, 27-29).

Although the GA03 dataset is based on size-fractionated particle samples collected by in-situ filtration rather than sinking particles collected by sediment traps, we can nonetheless examine whether there is a correlation between POC and ballast minerals in small or large particle size fractions. We found that POC concentration in large (>51 mm) particles was not consistently correlated with any of the potential ballast minerals CaCO3, opal, and lithogenic particles (4).The lack of strong correlations within this regional dataset is consistent with the idea that ballast mineral correlations with POC may only emerge in global datasets that combine different biogeochemical provinces (25).

Outlook

The GEOTRACES program is not only rapidly expanding global observations of dissolved TEIs, but it is also the latest major program to systematically sample particle distributions since JGOFS and GEOSECS (2). These particle measurements are not only helping us understand the processes controlling TEI distributions, but the TEI measurements can also be used as tracers for quantifying key processes of particle cycling. Both GEOTRACES and OCB can benefit from the insights gained in each program.

Author

Phoebe J. Lam (Department of Ocean Sciences, University of California, Santa Cruz)

References

1. GEOTRACES, Scientific Committee on Oceanic Research, Ed. (Baltimore, Maryland, 2006).
2. C. Jeandel et al., Progress in Oceanography 133, 6 (4//, 2015).
3. C. L. De La Rocha, in Treatise on Geochemistry, H. Elderfield, K. K. Turekian, Eds. (Elsevier, 2003), vol. 6: The Oceans and Marine Geochemistry, pp. 83-111.
4. P. J. Lam et al., Deep Sea Research Part II: Topical Studies in Oceanography 116, 303 (6//, 2015).
5. C. T. Hayes et al., Marine Chemistry 170, 49 (3/20/, 2015).
6. E. A. Boyle et al., paper presented at the 2016 Ocean Sciences Meeting, New Orleans, LA, USA, 2016.
7. C. Klaas, D. E. Archer, Global Biogeochemical Cycles 16, 1116 (Dec 5, 2002).
8. R. A. Armstrong et al., Deep-Sea Research Part II-Topical Studies in Oceanography 49, 219 (2002).
9. R. François et al., Global Biogeochemical Cycles 16, (Oct-Nov, 2002).
10 . L. S. Balistrieri, J. W. Murray, Geochimica Et Cosmochimica Acta 48, 921 (1984).
11. Z. Chase et al., Earth and Planetary Science Letters 204, 215 (Nov 30, 2002).
12. Z. Chase et al., Deep-Sea Research Part II-Topical Studies in Oceanography 50, 739 (2003).
13. S. D. Luo, T. L. Ku, Earth and Planetary Science Letters 220, 201 (Mar, 2004).
14. M. Roy-Barman et al., Earth and Planetary Science Letters 286, 526 (2009).
15. M. Roy-Barman et al., Earth and Planetary Science Letters 240, 681 (2005).
16. H. J. Walter et al., Earth and Planetary Science Letters 149, 85 (1997).
17. R. F. Anderson, M. P. Bacon, P. G. Brewer, Earth and Planetary Science Letters 66, 73 (1983).
18. H. N. Edmonds et al., Earth and Planetary Science Letters 227, 155 (Oct, 2004).
19. C. H. Lamborg et al., Philos T R Soc A, (accepted).
20. Y. Tang et al., paper presented at the 2016 Ocean Sciences Meeting, New Orleans, LA, USA, 2016.
21. O. Marchal, P. J. Lam, Geochimica Et Cosmochimica Acta 90, 126 (2012).
22. P. Lerner et al., Deep Sea Research Part I: Oceanographic Research Papers 113, 57 (7//, 2016).
23. P. J. Lam, O. Marchal, Annual Review of Marine Science 7, 159 (2015).

24. P. Boyd, T. Trull, Progress in Oceanography 72, 276 (2007).
25. J. D. Wilson, S. Barker et al., Global Biogeochemical Cycles 26, GB4011 (2012).
26. U. Passow, Geochemistry Geophysics Geosystems 5, (Apr 6, 2004).
27. P. J. Lam et al., Global Biogeochem. Cycles 25, GB3009 (2011).
28. S. A. Henson et al., Global Biogeochem. Cycles 26, GB1028 (2012).
29. S. Z. Rosengard et al., Biogeosciences 12, 3953 (2015).
30. D. C. Ohnemus, P. J. Lam, Deep Sea Research Part II: Topical Studies in Oceanography 116, 283 (6//, 2015).

Using GEOTRACES data to appraise iron cycling as represented within global ocean models

Posted by mmaheigan 
· Thursday, June 23rd, 2016 

We rely on global ocean models to predict how climate change might affect the evolution of ocean productivity, acidification, and deoxygenation (1). Such platforms are also used to test hypotheses regarding the controls on ocean biogeochemical cycling and to understand past change (both on historical and geologic timescales). Ocean biogeochemistry models began with relatively simple formulations of a carbon export flux that involved restoring to observed phosphate distributions, but have more recently evolved into complex multi-element representations of the ocean. In line with our understanding that the trace micronutrient iron (Fe) limits phytoplankton productivity over large areas of the world ocean (2), most global models that aim to project future change also explicitly represent the Fe cycle.

Datasets regarding the major limiting nutrients (nitrate, phosphate, and silicate) have been available as gridded ‘climatologies’ since the early 1990s (3). This has greatly facilitated the development and evaluation of modelled distributions over the past two decades. However, over this period there has been little comprehensive evaluation of how different models represent the ocean Fe cycle. Over recent years, there has been a marked increase in the availability of iron measurements in the ocean (4), largely driven by the international GEOTRACES effort to conduct full depth, basin-scale surveys. This led us to initiate the first-ever attempt to critically compare a range of global ocean iron models against the largest global datasets, as well as against the newly emerging ocean section data (5).

The Fe Model Intercomparison Project (FeMIP) sought to be as inclusive as possible in this first step and therefore did not seek to standardize the underlying ocean circulation or external inputs. Instead, we simply asked each of the thirteen models to provide their best representation of dissolved iron in three dimensions at monthly resolution. We then compared these models against each other, a global iron database of over 20,000 observations and against five unique basin-scale sections from the GEOTRACES intermediate data product 2014 (IDP2014) (6).

Firstly, it is apparent that even when the underlying iron cycles of the different models are evaluated, a substantial degree of inter-model discord exists. The total iron input varies from around 2 to 200 Gmol yr-1 across the thirteen models (Fig. 1a). Even for ‘well known’ sources like atmospheric deposition, the inter-model variability is around an order of magnitude. On the other hand, the average concentrations of dissolved Fe between the models is much less variable and ranges from 0.35 to 0.81 nmol L-1 (Fig. 1b), or an average of 0.58±0.14 nmol L-1. This apparent constancy reflects an initial view of the ocean Fe cycle in which interior Fe concentrations were held at a quasi-constant value of 0.6 nmol L-1 assuming a constant concentration of Fe-binding ligands (7). Thus the FeMIP models are balancing widely varying Fe input fluxes against relatively constant overall Fe concentrations by tuning the Fe scavenging rate, which is a crucial but poorly known parameter. This results in residence times for Fe that range from <5 to >500 years across the FeMIP models (Fig 1c). This difference is important, as it represents substantial inter-model deviation concerning the timescales over which the different models respond to a perturbation in Fe supply.

When compared statistically against the global dataset, similar levels of variability arise. Some models display correlation coefficients of >0.5, whereas others are slightly anti-correlated. When the FeMIP models are compared against the five GEOTRACES sections, it becomes apparent that those models that represent the newly emerging features of the iron cycle perform much better. For instance, having Fe scavenging rates that vary in space and time, including variable Fe:carbon (C) stoichiometry, multiple Fe sources, and representing ligand concentrations in a dynamic manner, all act to improve the representation of different observed features in the models. Importantly, the IDP2014 provided the opportunity to demonstrate that the issues at hand were specific to Fe, since the models could represent the observed distributions of major nutrients with a much greater degree of skill (5).

The next stage of FeMIP will be a deeper comparison of the processes themselves. Of particular interest is whether GEOTRACES datasets can provide broader assessments of the rates of Fe scavenging – e.g., using other particle-reactive, non-biological tracers such as thorium (8, 9). Equally, the emerging database of GEOTRACES process studies provides an important opportunity to appraise the way different models represent biological iron cycling and in particular, the often-observed importance of recycled and remineralized sources of Fe (10, 11). Finally, the new GEOTRACES intermediate data product 2017 will also facilitate further evaluation of models, providing new section data from the Atlantic, Pacific and Arctic Oceans.

Author

Alessandro Tagliabue (Dept. of Earth, Ocean and Ecological Sciences, School of Environmental Sciences, University of Liverpool)

References

1. L. Bopp et al., Biogeosci. 10(10), 6225-6245, doi:10.5194/bg-10-6225-2013 (2013).
2. C. M. Moore et al., Nature Geosci., doi:10.1038/ngeo1765 (2013).
3. S. Levitus et al., Prog. Oceanogr. 31(3), 245-273, doi:10.1016/0079-6611(93)90003-v (1993).
4. A. Tagliabue et al., Biogeosci. 9(6), 2333-2349, doi:10.5194/bg-9-2333-2012 (2012).
5. A. Tagliabue, A. et al., Glob. Biogeochem. Cycles, doi:10.1002/2015gb005289 (2016).
6. E. Mawji et al., Marine Chem. 177, 1-8. doi:10.1016/j. marchem.2015.04.005 (2015).
7. K. S. Johnson, R. M. Gordon, K. H. Coale, Marine Chem. 57(3-4), 137-161, doi:10.1016/s0304-4203(97)00043-1 (1997).
8. C. T. Hayes et al., Geochim. Cosmochim. Acta 169, 1-16, doi:10.1016/j.gca.2015.07.019 (2015).
9. N. Rogan et al., Geophys. Res. Lett. 43(6), 2732-2740. doi:10.1002/2016gl067905 (2016).
10. P. W. Boyd et al., Glob. Biogeochem. Cycles 29(7), 1028-1043, doi:10.1002/2014gb005014 (2015).
11. R. F. Strzepek et al., Glob. Biogeochem. Cycles 19(4), GB4S26, doi:10.1029/2005gb002490 (2005).

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