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Particle settling rates increase with depth in the ocean
Deep-Sea Research II 49 (2002) 237–251
Particle settling rates increase with depth in the ocean William M. Berelson* Department of Earth Sciences, University of Southern California, Los Angeles, CA 90089-0740, USA Accepted 14 June 2001
Abstract Time and depth-scales of particulate organic carbon degradation and CaCO3 and biogenic opal dissolution are critical to understanding the depth distribution of CO2 and dissolved nutrients in the ocean. The speed at which particles sink and the factors that control sinking speed are of primary importance to the distribution of oceanic nutrient concentrations and to the preservation of biogenic material in the sediment record. Sequencing sediment trap collectors deployed at US JGOFS sites in the equatorial Pacific and Arabian Sea provided a time series of particle fluxes from which particle settling rates were estimated. A comparison of settling velocities obtained from 100–500 m (Pilskaln et al., Deep Sea Research 45 (1998) 1803) to settling velocities obtained for depths between 1000 and 3500 m indicate an increase of a factor of 2–10 between 100 and 2000 m and an increase of 15–60% between 2000 and 3500 m. The increase in settling velocity in the deep ocean is generally correlated with the loss of Corg with depth. Lithogenic content does not appear to impact particle settling rate. Variability in particle settling rate is systematically related to physical forcing of the surface ocean in the Equatorial Pacific, but not in the Arabian Sea. The increase in particle settling rate with depth likely influences the delivery of CaCO3 to the sea floor. r 2001 Elsevier Science Ltd. All rights reserved.
1. Introduction The rain of biogenic and other particles from the surface ocean to the sea floor usually involves the diagenetic loss of the most labile forms of particulate matter and changes in the shape and size of falling material (Martin et al., 1987; Silver and Gowing, 1991). Settling rate is an important factor regulating the amount of degradation which occurs to biogenic particles (Gnanadesikan, 1999), yet little is known about controls of particle settling speed or how surface ocean forcing or ballasting may impact settling velocities. Ballasting is central to the controversy regarding opal accumulation in Southern Ocean sediments (Kumar et al., 1995; Francois et al., 1997), i.e. iron *Corresponding author. Tel.: +1-213-740-5828; fax: +1-213-740-8801. E-mail address: [email protected] (W.M. Berelson). 0967-0645/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 7 - 0 6 4 5 ( 0 1 ) 0 0 1 0 2 - 3
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
supply might relate to diatom test density and thereby influence settling rate and preservability (Boyle, 1998). Particle settling characteristics have been determined for the upper 500 m primarily by observation of divers and with remote camera/video systems (Shanks and Trent, 1980; Asper, 1987; Alldredge and Gotschalk, 1989; Pilskaln et al., 1998). The recent, widespread use of sediment traps with multiple collector cups permits determination of daily to weekly rain rates and tracking of flux events through the ocean interior as multiple traps are generally deployed on a single mooring. Most traps used on the US JGOFS process studies of the Equatorial Pacific (EqPac) and the Arabian Sea (AS) had 21 cup collectors (Honjo et al., 1995, 1999). This number of cup collectors helps in resolving export events generated by changes in surface ocean conditions (Deuser et al., 1990). Tracking flux events between trap depths has defined settling velocities at about 70–180 m/day, and identified times when particle settling rates increase with depth (Honjo and Manganini, 1993; Honjo et al., 1995, 1999). However, this approach is not always successful as there are many examples where a peak in flux at a shallower depth does not produce a peak in flux at a deeper depth (Fig. 1). Examining the offset between flux peaks does allow for the study of individual constituent settling rates, but it is dependent on the faithful record of each trap cup, and sometimes a trap cup fails. Another concern with this approach is whether peak flux events are representative of average conditions. A third concern pertains to the accuracy with which a given trap collects raining particles at any given time. Recent analysis of 230Th and 231Pa budgets indicate that deep sediment traps may be subject to systematic over- or under-trapping (Yu et al., 2000). In particular, traps moored in water depths of 800–1200 m tend to show systematic undertrapping. It is the potential for a trap flux to yield slightly or significantly inaccurate results that led to the following analysis of settling rates by tracking flux ratios, rather than rain rates. In this study I have synthesized the sediment trap data originally generated by Honjo, Dymond and Collier (US JGOFS PI’s) and manipulated this data to yield information regarding particle settling rates. Settling rates in two oceanic regions are compared and viewed in terms of ballasting and surface ocean forcing.
2. Methodology and results The premise of this study is that there are temporal variations in elemental ratios of raining material and that these ratios should be preserved during settling. This approach is different from tracking flux peaks because it (1) normalizes fluxes of one element to another and (2) relies on the relationship between all the trap cups, not just between two cups. I determined the molar ratio of organic carbon (Corg), biogenic Si and CaCO3 to Al, hence using Al as a normalization standard, but also ratioed the biogenic elements to each other. Ratios were calculated for nearly every cup from US JGOFS EqPac and AS moorings. The EqPac sediment trap cup data is from Honjo et al. (1995) and the AS data was available on the US JGOFS web-site (http://usjgofs. whoi.edu/) and from Honjo et al. (1999). These two publications contain information pertaining to the trap location and details of the chemical analyses; where mass fluxes were reported, they were converted to molar fluxes.
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 2
4
Sta 3 (NEM) 858 m
Carbonate Rain (mmol/m2d)
Corg Rain (mmol/m2d)
3.5
Sta 3 (NEM) 858 m
3 2.5 2 1.5 1
1.5
1
0.5
0.5
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cup #
Cup # 2
4
Sta 3 (NEM) 1857 m
Carbonate Rain (mmol/m2d)
Corg Rain (mmol/m2d)
3.5
Sta 3 (NEM) 1857 m
3 2.5 2 1.5 1
1.5
1
0.5
0.5 0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cup #
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cup #
Fig. 1. Bar graph of Corg and CaCO3 fluxes into individual cups from sediment traps located at AS-3. One trap (top two panels) was located at 858 m below the sea surface, a second trap (lower panels) 1857 m below sea surface. Traps were deployed during the NE Monsoon period. The peak in Corg and CaCO3 rain into cup #2 as seen in the shallow trap is not clearly recognizable in the deeper trap. This is an example of a time when tracking flux maxima does not yield clear information about particle settling velocities.
Following calculation of flux ratios, I determined the fit of a linear regression of one ratio in each of the upper trap cups to the same ratio in the same cup in a lower trap. In cases where the timing between trap cup intervals changes, the analysis breaks down. This occurs when a trap is programmed to have cup cycles of variable duration. Only traps that recorded fluxes for a consistent cup-cycle interval were used. The best linear fit was defined by the linear regression correlation coefficient, ‘r’. In making the regression analysis, it was important to compare as large a set of ratio data as possible, hence traps with fewer than 6 cup intervals were excluded from this analysis.
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A trap cup collects material over a specific interval of time; the traps analyzed in this study cycled every 8.5 or 17 days. The linear regression tests whether the ratio of material falling through the ocean at one depth is synchronously related to the ratio of material falling deeper. The fit of this linear regression was modified by comparing upper trap cups to lower trap cups offset by m (m ¼ 0; 1, 2, 3, etc.) rotations (Fig. 2). It was possible to determine the timing between material arriving in the upper cup and material arriving in the deeper cup by determining the number of cup rotations necessary before the best fit in a linear regression was achieved. The correlation of particle constituent ratios between two depths as a measure of rain rate relies on a number of assumptions about how particles fall through the ocean. One assumption is that particles are raining vertically between 800 and 3500 m. The modeling of Siegel and Deuser (1997) suggests that traps at any one depth collect material produced in a large area of the surface ocean overlying the trap location. However, the coherence of flux peaks in successive traps and the pattern of sediment composition and accumulation rate in the Equatorial Pacific, which mirrors surface ocean equatorial symmetry (Murray and Leinen, 1993), suggest that this assumption may be approximately valid for deep sinking particles. Another assumption is that the constituents ratioed are all part of the same particle. If this assumption were false, the ratio analysis I present below would yield non-systematic results, which is largely not the case. A third assumption is that there are naturally driven changes in the production of material with different ratios and that this variability will persist as particles fall through the water column. The last assumption does not ignore the diagenetic alteration of material as it falls but requires that the diagenetic process does not fully eliminate any temporal changes in source ratios. The methodology employed in this study allows for diagenetic variability, but assumes that when a ratio is large in the upper trap, it will be large in the lower trap. Uncertainties in the amount of diagenetic alteration that takes place during particle settling and the possibility that not all of the constituents measured are always part of the same particle make it paramount that a suite of different constituent ratios be analyzed. The summary of regression analyses in Table 1 indicate the importance of this strategy. Linear regression analyses yield coefficients that imply statistical significance; the closer the ‘r’ value is to 1.0, the less likely that the two parameters show correlation due to chance. The value of ‘r’ is dependent on the correlation of the two ratios and the number of points used in the regression. The number of cups available for each trap pair is given (n) in Table 1, however, for any given elemental ratio and particular comparison, the number of points may be a few more or a few less than n: Thus each value of ‘r’ was judged against a statistical table (Breiman, 1973; Taylor, 1997) and deemed significant at a cut-off level a ¼ 0:02 taking into account the number of data available. This is a rigorous criteria level for deciding significance, and not all regressions produced correlation coefficients that were deemed significant. However, running the regression for different cup rotations always produced a maximum or best correlation coefficient. Thus, there were two criteria for choosing the optimal number of trap cup rotations; examining which regressions were significant and examining which regressions were the best. Sometimes the regression correlation coefficient changes considerably between successive cup shifts (e.g. Corg/Al for AS-3, NE Monsoon, 858–1857 m), and sometimes the difference in this parameter is negligible through several cup shifts (e.g. Corg/Al for AS-2, SW Monsoon, 924– 1996 m). Hence, a comparison of regressions for a suite of ratios is the method employed in this study for establishing particle rain rates. To test for the possibility that material in the shallow
241
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 No Shift
No Shift
2
1.5
r = 0.88 FSi/FCo r g (2284 m)
FSi/FCo r g (880 m)
r = 0.55 1
0.5
0
0
0.5 1 FSi/FCo r g (2284 m)
1.5 1 0.5 0
1.5
One Cup Shift
0
r = 0.71 FSi/FCo r g (2284 m)
FSi/FCo r g (880 m)
r = 0.71 1
0.5
0
0.5 1 FSi/FCo r g (2284 m)
1.5 1 0.5 0
1.5
0
0.5 1 1.5 FSi/FCo r g (3600 m)
Two Cup Shift r = 0.65 FSi/FCo r g (2284 m)
FSi/FCo r g (880 m)
r = 0.70 1
0.5
0
2
Two Cup Shift
2
1.5
0
2
One Cup Shift
2
1.5
0
0.5 1 1.5 FSi/FCo r g (3600 m)
0.5 1 FSi/FCo r g (2284 m)
1.5
1.5 1 0.5 0
0
0.5 1 1.5 FSi/FCo r g (3600 m)
2
Fig. 2. Linear regressions of Si/Corg in sediment trap cups from a shallow trap to cups in a deeper trap for a mooring located in the Pacific Ocean, at the Equator, 1401W. The first column shows this ratio for traps located at 880 and 2284 m, the second column shows traps at 2284 and 3618 m. The cup cycle is 17 days, and the best linear regression occurs with a one-cup-shift for the shallow to mid-level traps but with no-shift for the mid-level to deep trap pair.
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
Table 1 EqPac sediment trap cup rotations necessary to get the best linear relationship (r) between ratios of biogenic material. Cup rotations represent 17 day cycles Station EqPac
Depth (m)
Equator
880–2284 n ¼ 17
Equator
Ratio
ra No-shift
1-cup
2-cup
Settling velocity (m/day)
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.62* 0.53+ 0.20 0.55 0.15
0.64* 0.01 0.45+ 0.71* 0.32+
0.72* 0.03 0.32 0.70* 0.21
83
2284–3618 n ¼ 18
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.91* 0.58* 0.65* 0.88* 0.61*
0.65* 0.07 0.02 0.71* 0.12
0.72* 0.01 0.07 0.65* 0.12
157
51N
1191–2091 n ¼ 19
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.47+ 0.87* 0.56* 0.70* 0.72*
0.02 0.80* neg 0.84* 0.55*
0.08 0.72* 0.07 0.59* 0.55*
106
51N
2091–3793 n ¼ 13
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.44+ 0.83* 0.37+ 0.81* 0.80*
0.25 0.81* 0.21 0.66 0.55*
0.39 0.72* neg 0.84* 0.45
200
51S
1216–2316 n ¼ 11
Si/Corg Si/Ca
0.84* 0.93*
neg neg
0.50 0.10
129
121S
1292–3594 n ¼ 19
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.11 0.11+ 0.40+ 0.43+ 0.79*
0.17+ neg 0.02 0.33 0.51
0.16 0.02 0.01 0.26 0.29
271
No-shift
1-cup
2-cup
3-cup
4-cup
ra Arabian Seab 2-NE
903–1974 n ¼ 14
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.60+ 0.81* 0.65* 0.46+ 0.43
0.59 0.43 0.55 0.26 0.62+
0.51 0.01 0.40 0.20 0.21
0.23 neg 0.13 neg neg
neg neg 0.38 neg 0.02
252
1974–3141 n ¼ 13
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.80* 0.70* 0.73* 0.47 0.11
0.75* 0.90* 0.67* 0.50+ 0.51+
0.56 0.81* 0.45 0.50 0.36
0.19 0.19 0.16 0.06 0.10
0.12 0.14 neg 0.24 0.19
137
243
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 Table 1 (Continued) Station
2-SW
3-NE
3-SW
4-NE
4-SW
Depth (m)
Ratio
ra No-shift
1-cup
2-cup
3-cup
4-cup
Settling velocity (m/day)
924–1996 n¼6
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.97* 0.90* 0.95* 0.98* 0.82
0.97* 0.83 0.99* 0.92 0.93
0.85 0.36 0.89 0.91 0.99*
0.83 0.86 0.88 0.81 0.97*
0.18 0.74 0.38 0.05 0.21
252
1996–3159 n¼7
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.95* 0.90* 0.97* 0.96* 0.96*
0.96* 0.79 0.93* 0.89 0.87
0.56 0.32 0.61 0.70 0.50
0.19 0.03 0.12 0.08 0.09
neg neg neg neg neg
274
858–1857 n ¼ 10
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.74+ 0.94* 0.84* 0.77* 0.07
0.51 0.78* 0.90* 0.88* 0.54
0.03 0.39 0.58 0.61 0.10
neg neg 0.19 0.55 0.53
neg neg neg 0.26 0.63+
235
1857–2966 n ¼ 18
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.89* 0.96* 0.68* neg neg
0.67* 0.83* 0.51 neg 0.32
0.54 0.57 0.38 neg 0.24
0.44 0.33 0.39 0.36+ 0.58+
0.02 neg 0.02 0.27 0.36
261
888–1882 n¼7
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.79 0.48 0.64 0.26 0.00
0.92* 0.77 0.75+ 0.35+ 0.38+
0.73 0.71 0.72 0.06 neg
0.39 0.82+ 0.37 neg neg
0.07 0.63 0.19 neg neg
117
1882–2991 n ¼ 10
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.56+ 0.58+ 0.78* 0.76* 0.79*
0.44 0.49 0.56 0.78* 0.50
neg 0.25 neg 0.07 0.02
neg neg neg 0.01 neg
neg neg neg 0.07 neg
261
821–2229 n ¼ 18
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.85* 0.69* 0.75* 0.65* 0.63*
0.65* 0.44 0.78* 0.52 0.58*
0.14 0.55 0.52 0.35 0.59*
0.04 0.81* 0.11 0.22 0.53
neg 0.40 neg 0.02 0.30
331
2229–3478 n ¼ 18
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.73* 0.77* 0.74* 0.83* 0.86*
0.86* 0.49 0.72* 0.75* 0.90*
0.35 0.28 0.36 0.56 0.68*
0.17 0.06 neg 0.41 0.49
0.08 neg neg 0.34 0.34
294
2215–3489 n ¼ 11
Corg/Al Ca/Al Si/Al
0.26 0.85* 0.70*
0.66+ 0.36 0.89*
0.23 0.06 0.91*
0.40 neg 0.15
0.33 neg neg
300
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
Table 1 (Continued) Station
5
Depth (m)
Ratio
ra No-shift
1-cup
2-cup
3-cup
4-cup
Settling velocity (m/day)
Si/Corg Si/Ca
0.78* 0.72*
0.92* 0.70
0.81* 0.71
0.10 0.17
neg neg
800–2363 n¼7
Corg/Al Ca/Al Si/Al
0.43 0.36 0.56
0.49+ 0.62+ 0.56+
neg 0.33 neg
neg neg neg
neg 0.24 neg
92
2363–3915 n ¼ 16
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.71* 0.61* 0.38 0.68* 0.69*
0.58 0.11 0.52+ 0.81* 0.52
0.59 0.14 0.23 0.52 0.65
0.44 neg 0.19 0.31 0.23
0.52 neg 0.08 0.15 0.18
183
a Asterisks indicate the correlation coefficient which are significant at the a ¼ 0:02 level. +sign indicates correlations which are not significant at this level, but are the best correlation. ‘neg’ indicates regressions that show a negative correlation. Ca represents CaCO3, all ratios were molar ratios. n is the average number of points in the regression. b Cup rotations represent 8.5 day cycles except at Station 5 where rotations occur every 17 days. NE and SW denote Monsoonal season.
trap was raining to the deeper trap over a long time lag, many trap cup rotations (up to 9) were used in the regression analysis. The data reported in Table 1 represent the best positive correlations found. Settling velocities were determined by considering the spacing between traps and the time lag, or number of cup-shifts, necessary to yield the best linear correlation. The best linear correlation was determined using the following criteria. The number of cup rotations for which there were the most statistically significant correlation coefficients was deemed the most important criterion. The number of best fit correlation coefficients was deemed the second most important criterion. Thus, for EqPac, 51S, only the no-shift regression yielded statistically significant correlations. At the equator (2284–3618 m), several cup rotation scenarios yielded statistically significant correlations, but the no-shift option yielded the most significant correlations. In the AS-5 (800–2363 m) trap pair, no statistically significant correlation coefficient was found, yet for all element ratios, the best linear regression was found when the lower trap was subjected to a one-cup rotation. A final example is given by the AS-2 trap pair (NEM, 1974–3141 m). The same number of significant correlations are found for the no-shift and one-shift regressions. No significant correlations are found in analyzing the Si/Corg and Si/Ca ratios, but for these two cases, the one cup-shift regression yields a better correlation coefficient than do the other options. In this example, a onecup time lag is deemed the most appropriate for determination of settling velocity. If, as was stated earlier, particles with different compositions settled at quite different rates, there would be systematic offsets between the number of cup rotations necessary to optimize the correlation coefficient for different element ratios. Such a systematic difference was not observed. In most cases, the optimal regression coefficient for one elemental ratio is the same for other element ratios. When the best linear regression fit predicts a different cup-shift for one ratio
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
245
compared to another ratio, the entire series of ratios is evaluated and the offset most often represented by the statistically significant fit was chosen. Defining the number of cup rotations necessary to optimize the regression coefficient for a number of element ratios does not, in and of itself, define the time lag between particles arriving at the upper and lower traps. At the EqPac sites, when a one-cup shift results in the best set of ‘r’ values, it is assumed that this represents a 17-day offset between material reaching the upper trap and material reaching the deeper trap. Thus the predicted settling velocity for particles falling at the Equator between 880 and 2284 m is 1404/17=83 m/day. In many cases, the no-shift option presents the best set of correlation coefficients. Under this condition, the settling velocity may be infinitely fast. However, if settling were extremely fast, the no-shift regression would be the best fit for traps located at all depths. The very fast settling hypothesis was tested by determining the best fit regression between the shallowest and deepest traps. At all sites where two sets of traps indicate rapid particle settling (no-shift regression), this analysis showed that settling between the upper-most and lower-most traps required a one-cup rotation. This means that settling velocity between the shallowest and deepest traps is constrained; in the case of 51N at EqPac, the predicted velocity between 1191 and 3793 m would be 2602 m/ 17day=153 m/day. This, however, does not, imply that particles settling between 1191 and 2091 m and particles settling between 2091 and 3793 m are all falling at this rate. In fact, this could not be the case because the spacing between trap pairs requires that particles fall faster than 153 m/day between the deeper traps and slower than 153 m/day in the shallower traps. An assumption was made that the travel time was 8.5 days for the 900 m between the upper traps and 8.5 days for the 1702 m between the lower traps. This is consistent with a one-cup (17-day) offset between the shallowest and deepest cups. There is room in this analysis for velocities to be greater than estimated, as a one-cup rotation implies an average offset of 17 days but may actually represent a o17-day offset. Choosing the cup cycle length as the lag time when the regression analysis suggests a one-cup offset is the most reasonable estimate of average lag time. When there is no offset between cups in adjacent traps, the most reasonable assumption is that the lag is 0.5*cup cycle. This probably establishes a lower estimate of particle settling rate, but this approach yields results consistent with velocities established for particles falling from the shallowest to the deepest trap.
3. Discussion The range of settling velocities established by this method is similar for both the EqPac and AS regions; 80–270 m/day and 90–330 m/day, respectively (Fig. 3). Both pairs of traps from EqPac, from the equator and 51N, indicate an increase in settling velocity between B1000–2200 m and B2200–3500 m. The trap pair at 121S spans 1200–3500 m and suggests a settling velocity of >250 m/d. Although there is considerable uncertainty in the quantification of particle settling rate using the procedure outlined above, the limited data from EqPac suggest that particle settling rates in the deep ocean increase by B60%. Traps from the Arabian Sea show a similar pattern whereby most trap pairs indicate greater settling velocities at depths >2000 m compared to traps located at 800–2000 m. This pattern is not without exceptions, yet most trap pairs (4 out of 6) indicate an increase in particle settling rate
246
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
Settling Velocity (m/d) 0
50
100
150
200
250
300
350
0 Equatorial Pacific 500 EQ
1000
5 S
12 S
Depth (m)
1500 5 N
2000 5 N
2500
3000
3500
a
EQ
4000
Settling Velocity (m/d) 0
50
100
150
200
250
300
350
0 Arabian Sea 500
1000
Depth (m)
1500
2000
2500
3000
Sta. 2 Sta. 3
3500
Sta. 4 Sta. 5
b
4000
Fig. 3. (a) Particle settling velocities for EqPac traps. (b) Particle settling velocities for traps in the AS. Open symbols designate traps deployed during the NE Monsoon period, solid symbols denote traps deployed during the SW Monsoon period. The traps at Sta. 5 were operating over both periods.
247
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
with depth. The velocities obtained from the AS analyses are generally greater than the velocities obtained from the analysis of EqPac data, but this is expected given that the cup cycles were 17 days for EqPac and 8.5 days for AS (Sta. 2–4). The greater frequency of cup cycling permits greater resolution of faster settling particles. The AS trap data were divided into deployments during the NE Monsoon period (Nov. 94– April 95) and SW Monsoon period (May 95–Oct. 95). There was no systematic difference in settling velocities between these periods even though the SW Monsoon particle rain contained a greater fraction (wt%) and a greater mass flux of lithogenic particles (Honjo et al., 1999). This leads to the hypotheses that (1) monsoonal forcing and (2) the presence of lithogenic ballast do not influence particle settling rates. This second point is perhaps best illustrated by comparing AS5 to EqPac stations at 51N and 51 S. The traps at AS-5 rotated on a 17-day cycle, like the EqPac traps, and the velocities obtained for this trap pair are identical to the velocities obtained at the 51 N and 51S sites. Although mass rain rates at these two sites were similar (Table 2), the AS-5 station had a greater fraction of lithogenic material compared to the EqPac stations. This adds further support to the hypothesis that ballasting by lithogenic particles does not control particle settling velocity. Traps from the EqPac region did show a systematic difference in settling rate with surface ocean forcing (Fig. 4). For this analysis, the EqPac trap data were divided mid-year in 1992, the first half corresponding to El Nino conditions and the second half representative of non-El Nino conditions (Kessler and McPhaden, 1995). Higher settling velocities, by a factor of 2, were recorded during the El Nino phase of forcing at 1401W. Also, it appears that stations off the equator have greater settling velocities than stations near the equator. This seems counterintuitive, as one might expect regions or sites of greater particle production (e.g. near the equator and during non-El Nino periods) to yield particles that sink at greater rates. It appears that primary productivity is negatively correlated with particle settling velocity, although more data and longer trap records would help confirm this surprising result.
Table 2 Properties of sediment traps and constituents. Flux and wt% values represent annual averages for the deepest two sediment traps Station ID
Cup cycle (day)
Mass flux (mg/m2day)
wt% Corg
CaCO3
Bio SiO2
Other
Equatorial Pacific Equator 51N 51S 121S
17 17 17 17
100 75 65 25
4.5 5.3 4.2 3.4
66 66 69 78
25 22 23 13
5 7 4 5
Arabian Sea Sta. 2 Sta. 3 Sta. 4 Sta. 5
8.5 8.5 8.5 17
234 220 163 62
6.6 6.6 6.1 5.7
50 48 51 61
19 19 18 11
24 26 25 22
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
Approx.Settling Velocity (m/day)
200 El Nino Non-El Nino 150
100
50
0 Eq. (880-2284)
Eq. (2284-3618)
5˚N (1191-2091)
Location (depth m)
Fig. 4. Settling velocities for particles as derived from sediment trap analyses by dividing the EqPac trap deployment period into one half a year (Jan–Aug, 1992), considered to be influenced by El Nino equatorial conditions, and one half a year (Aug–Dec, 1992) considered to be dominated by forcing that represented non-El Nino conditions.
The difference in settling velocity between shallow and deep traps is not as apparent in the AS data as in the EqPac data; Fig. 5 provides a comparison of both sites with all traps averaged. The pattern of a greater velocity gradient at EqPac relative to AS is interesting in light of the difference in wt% organic carbon (Corg) between these sites. Material falling through the ocean interior at the Arabian Sea generally contains a greater fraction of Corg than material raining to the sea floor in the Equatorial Pacific (Table 2). In a paper that also appears in this volume, Armstrong et al. (2002) have shown that at EqPac there is a steeper gradient of wt%Corg with depth than there is at the AS sites. More Corg is lost from particles falling between 1000 and 3500 m at EqPac than is lost from particles falling through the same depths at the AS. The steeper velocity gradient at EqPac relative to the gradient at the AS sites may be related to this pattern of Corg loss, but it is uncertain at this time whether settling velocity affects Corg content or Corg content affects settling velocity. The general picture is obscured in a detailed image of Corg and settling velocity (Fig. 6). Although the trend in all trap analyses is for a loss of Corg with depth, the traps that exhibit the greatest change in organic carbon content do not always exhibit an increase in settling velocity. An increase in particle settling rate may be caused by several different mechanisms; e.g., all particles may sink at the same speed but are intercepted less frequently with depth. Particles may be repackaged at depth and become denser. Previous studies have noted changes in classes of fecal pellets present at different depths (Honjo, 1978; Urrere and Knauer, 1981), suggesting reworking of sinking material by deep-living heterotrophs (Karl et al., 1988, Wishner et al., 1998). Alternatively, particles may grow in size as they sink, thereby increasing the sinking rate. According to calibrations of particle size and settling velocity (Alldredge and Gotschalk, 1988), a change in particle settling velocity from 100 to 200 m/day would correspond with an increase in particle diameter of about an order of magnitude, from B10 to B100 mm. Another mechanism might involve the loss of the slowest sinking particles from the spectrum of slow and fast sinking
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
249
Settling Velocity (m/d) 0
100
50
150
200
250
300
350
0
500
Depth range (m)
1000
? ?
1500
2000
2500
3000
3500
Arabian Sea EqPac
4000
Fig. 5. The average settling velocity for upper to mid-level and mid-level to deep traps in the AS and EqPac regions. Vertical range denotes depth range of traps. Horizontal bar denotes the s.e. of the mean for 3 shallow and 2 deep trap pairs at EqPac and 6 shallow and 7 deep trap pairs for the AS. The lines in the upper left indicate the particle settling velocities determined by Pilskaln et al. (1998) for Central California coastal water between 100–500 m and the range in velocities reported by Pilskaln et al. (1998) by workers who have looked at particle settling rates in the surface (upper 40 m) ocean. There is no certainty that the settling velocities determined for coastal settings is similar to settling velocities in the EqPac and AS regions, thus the dotted lines are drawn with a question mark.
particles. If this latter mechanism were responsible, the distribution of particles in the deep ocean would reflect the profile of settling velocity. The conclusion of this study is that particle settling rates increase with depth in the ocean in a systematic fashion (Fig. 5). The strength of this conclusion is drawn from the analyses and data presented by Pilskaln et al. (1998) in which particle settling rates were analyzed at a continental margin site for depths 100–500 m. They found mean rates varied between 16 and 25 m/day and argue that other estimates of particle sinking rates in shallow water (o500 m) may be biased by observation of mostly fast sinking particles. Thus, the trend line shown in Fig. 5 expresses a dramatic change in particle sinking rate between the upper and deep ocean. Such a pattern, and whether it has a linear or curvilinear functionality, will be relevant and important in the formulation of GCM’s in which particle remineralization rates within the water column must be parameterized. For example, the ocean becomes more corrosive to CaCO3 as a function of depth; thus one would expect particulate carbonate to dissolve more readily as it sinks deeper. Yet an analysis of trap data shows that the wt% CaCO3 lost during transit from 1000 to 2000 m is similar to or more than the wt% lost during transit from 2000 to 3500 m (Tsunogai and Noriki, 1991). In
250
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 Arabian Sea Stations 2-4 400 350
Sta. 4
Settling Velocity (m/day)
300 250
Sta. 3
Sta. 2
200 Sta. 3
150
Sta. 2
100 50 0 5
6
7 8 Average wt. % Corg
9
10
Fig. 6. Settling velocity and average wt% organic carbon content of trap material for Arabian Sea Stations 2, 3 and 4. The points with bullseyes represent traps deployed during the SW Monsoonal period, other points indicate deployment during the NE Monsoonal period. Arrows indicate the upper and lower trap in a sequence. All trap pairs show a decline in %Corg from the shallower to deeper trap, as all arrows move to the left in the x-axis domain. In some cases a small change in wt% Corg corresponds with a large change in settling velocity (Sta. 3 SW Monsoon) in other traps a large change in wt% Corg has a small effect on settling rate (Sta. 3 NE Monsoon).
EqPac and AS traps, carbonate rain decreases by 8.9 wt%/km between 1000 and 2000 m and by 6.5 wt%/km between 2000 and 3500 m. An increase in particle settling rate can explain this apparent inconsistency. Acknowledgements I acknowledge the high quality of the trap data and thank S. Honjo, J. Dymond and R. Collier for making this data available. Thanks to D. Hammond, T. Michaels and two anonymous reviewers for their helpful comments. This is US JGOFS contribution number 694. References Alldredge, A.L., Gotschalk, C., 1988. In situ settling behavior of marine snow. Limnology and Oceanography 33, 339–351. Alldredge, A.L., Gotschalk, C., 1989. Direct observations of he mass flocculation of diatom blooms: characteristics, settling velocities and formation of diatom aggregates. Deep-Sea Research 36, 159–171. Armstrong, R.A., Lee, C., Hedges, J.I., Honjo, S., Wakeham, S.G., 2002. A new mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast mineral. Deep-Sea Research II 49, 219–236.
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251
Asper, V.L., 1987. Measuring the flux and sinking speed of marine snow aggregates. Deep-Sea Research 34, 1–17. Boyle, E.A., 1998. Pumping iron makes thinner diatoms. Nature 393, 733–734. Breiman, L., 1973. Statistics: with a view toward applications. Houghton-Mifflin Co., Boston, MA, 399pp. Deuser, W.G., Muller-Karger, F.E., Evans, R.H., Brown, O.B., Esaias, W.E., Feldman, G.C., 1990. Surface ocean color and deep-ocean carbon flux: how close a connection? Deep-Sea Research 37, 1331–1343. Francois, R., Altabet, M., Yu, E.-F., Sigman, D.M., Bacon, M.P., Frank, M., Bohrmann, G., Bareille, G., Labeyrie, L.D., 1997. Contribution of southern ocean surface-water stratification to low atmospheric CO2 concentrations during the last glacial period. Nature 389, 929–935. Gnanadesikan, A., 1999. A global model of silicon cycling: sensitivity to eddy parameterization and dissolution. Global Biogeochemical Cycles 13, 199–220. Honjo, S., 1978. Sedimentation of materials in the Sargasso Sea at a 5,367 m deep station. Journal of Marine Research 36, 469–492. Honjo, S., Manganini, S.J., 1993. Annual biogenic particle fluxes to the interior of the N Atlantic Ocean; studied at 341N 211W and 481N 211W. Deep-Sea Research 40, 587–607. Honjo, S., Dymond, J., Collier, R., Manganini, S.J., 1995. Export production of particles to the interior of the equatorial pacific ocean during the 1992 EqPac experiment. Deep-Sea Research 42, 831–870. Honjo, S., Dymond, J., Prell, W., Ittekkot, V., 1999. Monsoon-controlled export fluxes to the interior of the Arabian sea. Deep-Sea Research 46, 1859–1902. Karl, D.M., Knauer, G.A., Martin, J.H., 1988. Downward flux of particulate organic matter in the ocean: a particle decomposition paradox. Nature 332, 438–441. Kessler, W.S., McPhaden, M.J., 1995. The 1991–1993 El nino in the central Pacific. Deep-Sea Research 42, 295–333. Kumar, N., Anderson, R.F., Mortlock, R.A., Froelich, P.N., Kubik, P., Dittrich-Hannen, B., Suter, M., 1995. Increased biological productivity and export production in the glacial southern ocean. Nature 378, 675–680. Martin, J.H., Knauer, G.A., Karl, D.M., Broenkow, W.W., 1987. VERTEX: carbon cycling in the NE pacific. DeepSea Research 34, 267–285. Murray, R.W., Leinen, M., 1993. Chemical transport to the sea floor of the equatorial pacific ocean across a latitudinal transect at 1351W: tracking sedimentary major, trace and rare earth element fluxes at the equator and the intertropical convergence zone. Geochimica Cosmochimica Acta 57, 4141–4163. Pilskaln, C.H., Lehmann, C., Paduan, J.B., Silver, M.W., 1998. Spatial and temporal dynamics in marine aggregate abundance, sinking rate and flux: Monterey Bay, central California. Deep-Sea Research 45, 1803–1837. Shanks, A.L., Trent, J.D., 1980. Marine snow: sinking rates and potential role in vertical flux. Deep-Sea Research 27, 137–144. Siegel, D.A., Deuser, W.G., 1997. Trajectories of sinking particles in the Sargasso Sea: modeling of statistical funnels above deep-ocean sediment traps. Deep-Sea Research 44, 1519–1541. Silver, M.W., Gowing, M.M., 1991. The particle flux: origins and biological components. Progress in Oceanography 26, 75–113. Taylor, J.R., 1997. An Introduction to Error Analysis, The Study of Uncertainties in Physical Measurements. University Science Books, Sausalito, CA 327pp. Tsunogai, S., Noriki, S., 1991. Particulate fluxes of carbonate and organic carbon in the ocean. Is the marine biological activity working as a sink of atmospheric carbon? Tellus 43B, 256–266. Urrere, M.A., Knauer, G.A., 1981. Zooplankton fecal pellet fluxes and vertical transport of particulate organic material in the pelagic environment. Journal of Plankton Research 3, 369–387. Wishner, K.F., Gowing, M.M., Gelfman, C., 1998. Mesozooplankton biomass in the upper 1000 m in the Arabian Sea: overall seasonal and geographic patterns, and relationship to oxygen gradients. Deep-Sea Research 45, 2405–2432. Yu, E.-F., Francois, R., Bacon, M.P., Honjo, S., Fleer, A.P., Manganini, S.J., Rutgers van der Loeff, M.M., Ittekot, V., 2000. Trapping efficiency of bottom-tethered sediment traps estimated from the intercepted fluxes of 230Th and 231 Pa. Deep-Sea Research I 48, 865–889.
Particle settling rates increase with depth in the ocean William M. Berelson* Department of Earth Sciences, University of Southern California, Los Angeles, CA 90089-0740, USA Accepted 14 June 2001
Abstract Time and depth-scales of particulate organic carbon degradation and CaCO3 and biogenic opal dissolution are critical to understanding the depth distribution of CO2 and dissolved nutrients in the ocean. The speed at which particles sink and the factors that control sinking speed are of primary importance to the distribution of oceanic nutrient concentrations and to the preservation of biogenic material in the sediment record. Sequencing sediment trap collectors deployed at US JGOFS sites in the equatorial Pacific and Arabian Sea provided a time series of particle fluxes from which particle settling rates were estimated. A comparison of settling velocities obtained from 100–500 m (Pilskaln et al., Deep Sea Research 45 (1998) 1803) to settling velocities obtained for depths between 1000 and 3500 m indicate an increase of a factor of 2–10 between 100 and 2000 m and an increase of 15–60% between 2000 and 3500 m. The increase in settling velocity in the deep ocean is generally correlated with the loss of Corg with depth. Lithogenic content does not appear to impact particle settling rate. Variability in particle settling rate is systematically related to physical forcing of the surface ocean in the Equatorial Pacific, but not in the Arabian Sea. The increase in particle settling rate with depth likely influences the delivery of CaCO3 to the sea floor. r 2001 Elsevier Science Ltd. All rights reserved.
1. Introduction The rain of biogenic and other particles from the surface ocean to the sea floor usually involves the diagenetic loss of the most labile forms of particulate matter and changes in the shape and size of falling material (Martin et al., 1987; Silver and Gowing, 1991). Settling rate is an important factor regulating the amount of degradation which occurs to biogenic particles (Gnanadesikan, 1999), yet little is known about controls of particle settling speed or how surface ocean forcing or ballasting may impact settling velocities. Ballasting is central to the controversy regarding opal accumulation in Southern Ocean sediments (Kumar et al., 1995; Francois et al., 1997), i.e. iron *Corresponding author. Tel.: +1-213-740-5828; fax: +1-213-740-8801. E-mail address: [email protected] (W.M. Berelson). 0967-0645/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 6 7 - 0 6 4 5 ( 0 1 ) 0 0 1 0 2 - 3
238
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
supply might relate to diatom test density and thereby influence settling rate and preservability (Boyle, 1998). Particle settling characteristics have been determined for the upper 500 m primarily by observation of divers and with remote camera/video systems (Shanks and Trent, 1980; Asper, 1987; Alldredge and Gotschalk, 1989; Pilskaln et al., 1998). The recent, widespread use of sediment traps with multiple collector cups permits determination of daily to weekly rain rates and tracking of flux events through the ocean interior as multiple traps are generally deployed on a single mooring. Most traps used on the US JGOFS process studies of the Equatorial Pacific (EqPac) and the Arabian Sea (AS) had 21 cup collectors (Honjo et al., 1995, 1999). This number of cup collectors helps in resolving export events generated by changes in surface ocean conditions (Deuser et al., 1990). Tracking flux events between trap depths has defined settling velocities at about 70–180 m/day, and identified times when particle settling rates increase with depth (Honjo and Manganini, 1993; Honjo et al., 1995, 1999). However, this approach is not always successful as there are many examples where a peak in flux at a shallower depth does not produce a peak in flux at a deeper depth (Fig. 1). Examining the offset between flux peaks does allow for the study of individual constituent settling rates, but it is dependent on the faithful record of each trap cup, and sometimes a trap cup fails. Another concern with this approach is whether peak flux events are representative of average conditions. A third concern pertains to the accuracy with which a given trap collects raining particles at any given time. Recent analysis of 230Th and 231Pa budgets indicate that deep sediment traps may be subject to systematic over- or under-trapping (Yu et al., 2000). In particular, traps moored in water depths of 800–1200 m tend to show systematic undertrapping. It is the potential for a trap flux to yield slightly or significantly inaccurate results that led to the following analysis of settling rates by tracking flux ratios, rather than rain rates. In this study I have synthesized the sediment trap data originally generated by Honjo, Dymond and Collier (US JGOFS PI’s) and manipulated this data to yield information regarding particle settling rates. Settling rates in two oceanic regions are compared and viewed in terms of ballasting and surface ocean forcing.
2. Methodology and results The premise of this study is that there are temporal variations in elemental ratios of raining material and that these ratios should be preserved during settling. This approach is different from tracking flux peaks because it (1) normalizes fluxes of one element to another and (2) relies on the relationship between all the trap cups, not just between two cups. I determined the molar ratio of organic carbon (Corg), biogenic Si and CaCO3 to Al, hence using Al as a normalization standard, but also ratioed the biogenic elements to each other. Ratios were calculated for nearly every cup from US JGOFS EqPac and AS moorings. The EqPac sediment trap cup data is from Honjo et al. (1995) and the AS data was available on the US JGOFS web-site (http://usjgofs. whoi.edu/) and from Honjo et al. (1999). These two publications contain information pertaining to the trap location and details of the chemical analyses; where mass fluxes were reported, they were converted to molar fluxes.
239
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 2
4
Sta 3 (NEM) 858 m
Carbonate Rain (mmol/m2d)
Corg Rain (mmol/m2d)
3.5
Sta 3 (NEM) 858 m
3 2.5 2 1.5 1
1.5
1
0.5
0.5
0
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cup #
Cup # 2
4
Sta 3 (NEM) 1857 m
Carbonate Rain (mmol/m2d)
Corg Rain (mmol/m2d)
3.5
Sta 3 (NEM) 1857 m
3 2.5 2 1.5 1
1.5
1
0.5
0.5 0
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cup #
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Cup #
Fig. 1. Bar graph of Corg and CaCO3 fluxes into individual cups from sediment traps located at AS-3. One trap (top two panels) was located at 858 m below the sea surface, a second trap (lower panels) 1857 m below sea surface. Traps were deployed during the NE Monsoon period. The peak in Corg and CaCO3 rain into cup #2 as seen in the shallow trap is not clearly recognizable in the deeper trap. This is an example of a time when tracking flux maxima does not yield clear information about particle settling velocities.
Following calculation of flux ratios, I determined the fit of a linear regression of one ratio in each of the upper trap cups to the same ratio in the same cup in a lower trap. In cases where the timing between trap cup intervals changes, the analysis breaks down. This occurs when a trap is programmed to have cup cycles of variable duration. Only traps that recorded fluxes for a consistent cup-cycle interval were used. The best linear fit was defined by the linear regression correlation coefficient, ‘r’. In making the regression analysis, it was important to compare as large a set of ratio data as possible, hence traps with fewer than 6 cup intervals were excluded from this analysis.
240
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
A trap cup collects material over a specific interval of time; the traps analyzed in this study cycled every 8.5 or 17 days. The linear regression tests whether the ratio of material falling through the ocean at one depth is synchronously related to the ratio of material falling deeper. The fit of this linear regression was modified by comparing upper trap cups to lower trap cups offset by m (m ¼ 0; 1, 2, 3, etc.) rotations (Fig. 2). It was possible to determine the timing between material arriving in the upper cup and material arriving in the deeper cup by determining the number of cup rotations necessary before the best fit in a linear regression was achieved. The correlation of particle constituent ratios between two depths as a measure of rain rate relies on a number of assumptions about how particles fall through the ocean. One assumption is that particles are raining vertically between 800 and 3500 m. The modeling of Siegel and Deuser (1997) suggests that traps at any one depth collect material produced in a large area of the surface ocean overlying the trap location. However, the coherence of flux peaks in successive traps and the pattern of sediment composition and accumulation rate in the Equatorial Pacific, which mirrors surface ocean equatorial symmetry (Murray and Leinen, 1993), suggest that this assumption may be approximately valid for deep sinking particles. Another assumption is that the constituents ratioed are all part of the same particle. If this assumption were false, the ratio analysis I present below would yield non-systematic results, which is largely not the case. A third assumption is that there are naturally driven changes in the production of material with different ratios and that this variability will persist as particles fall through the water column. The last assumption does not ignore the diagenetic alteration of material as it falls but requires that the diagenetic process does not fully eliminate any temporal changes in source ratios. The methodology employed in this study allows for diagenetic variability, but assumes that when a ratio is large in the upper trap, it will be large in the lower trap. Uncertainties in the amount of diagenetic alteration that takes place during particle settling and the possibility that not all of the constituents measured are always part of the same particle make it paramount that a suite of different constituent ratios be analyzed. The summary of regression analyses in Table 1 indicate the importance of this strategy. Linear regression analyses yield coefficients that imply statistical significance; the closer the ‘r’ value is to 1.0, the less likely that the two parameters show correlation due to chance. The value of ‘r’ is dependent on the correlation of the two ratios and the number of points used in the regression. The number of cups available for each trap pair is given (n) in Table 1, however, for any given elemental ratio and particular comparison, the number of points may be a few more or a few less than n: Thus each value of ‘r’ was judged against a statistical table (Breiman, 1973; Taylor, 1997) and deemed significant at a cut-off level a ¼ 0:02 taking into account the number of data available. This is a rigorous criteria level for deciding significance, and not all regressions produced correlation coefficients that were deemed significant. However, running the regression for different cup rotations always produced a maximum or best correlation coefficient. Thus, there were two criteria for choosing the optimal number of trap cup rotations; examining which regressions were significant and examining which regressions were the best. Sometimes the regression correlation coefficient changes considerably between successive cup shifts (e.g. Corg/Al for AS-3, NE Monsoon, 858–1857 m), and sometimes the difference in this parameter is negligible through several cup shifts (e.g. Corg/Al for AS-2, SW Monsoon, 924– 1996 m). Hence, a comparison of regressions for a suite of ratios is the method employed in this study for establishing particle rain rates. To test for the possibility that material in the shallow
241
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 No Shift
No Shift
2
1.5
r = 0.88 FSi/FCo r g (2284 m)
FSi/FCo r g (880 m)
r = 0.55 1
0.5
0
0
0.5 1 FSi/FCo r g (2284 m)
1.5 1 0.5 0
1.5
One Cup Shift
0
r = 0.71 FSi/FCo r g (2284 m)
FSi/FCo r g (880 m)
r = 0.71 1
0.5
0
0.5 1 FSi/FCo r g (2284 m)
1.5 1 0.5 0
1.5
0
0.5 1 1.5 FSi/FCo r g (3600 m)
Two Cup Shift r = 0.65 FSi/FCo r g (2284 m)
FSi/FCo r g (880 m)
r = 0.70 1
0.5
0
2
Two Cup Shift
2
1.5
0
2
One Cup Shift
2
1.5
0
0.5 1 1.5 FSi/FCo r g (3600 m)
0.5 1 FSi/FCo r g (2284 m)
1.5
1.5 1 0.5 0
0
0.5 1 1.5 FSi/FCo r g (3600 m)
2
Fig. 2. Linear regressions of Si/Corg in sediment trap cups from a shallow trap to cups in a deeper trap for a mooring located in the Pacific Ocean, at the Equator, 1401W. The first column shows this ratio for traps located at 880 and 2284 m, the second column shows traps at 2284 and 3618 m. The cup cycle is 17 days, and the best linear regression occurs with a one-cup-shift for the shallow to mid-level traps but with no-shift for the mid-level to deep trap pair.
242
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
Table 1 EqPac sediment trap cup rotations necessary to get the best linear relationship (r) between ratios of biogenic material. Cup rotations represent 17 day cycles Station EqPac
Depth (m)
Equator
880–2284 n ¼ 17
Equator
Ratio
ra No-shift
1-cup
2-cup
Settling velocity (m/day)
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.62* 0.53+ 0.20 0.55 0.15
0.64* 0.01 0.45+ 0.71* 0.32+
0.72* 0.03 0.32 0.70* 0.21
83
2284–3618 n ¼ 18
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.91* 0.58* 0.65* 0.88* 0.61*
0.65* 0.07 0.02 0.71* 0.12
0.72* 0.01 0.07 0.65* 0.12
157
51N
1191–2091 n ¼ 19
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.47+ 0.87* 0.56* 0.70* 0.72*
0.02 0.80* neg 0.84* 0.55*
0.08 0.72* 0.07 0.59* 0.55*
106
51N
2091–3793 n ¼ 13
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.44+ 0.83* 0.37+ 0.81* 0.80*
0.25 0.81* 0.21 0.66 0.55*
0.39 0.72* neg 0.84* 0.45
200
51S
1216–2316 n ¼ 11
Si/Corg Si/Ca
0.84* 0.93*
neg neg
0.50 0.10
129
121S
1292–3594 n ¼ 19
Corg/Al Si/Al Ca/Al Si/Corg Si/Ca
0.11 0.11+ 0.40+ 0.43+ 0.79*
0.17+ neg 0.02 0.33 0.51
0.16 0.02 0.01 0.26 0.29
271
No-shift
1-cup
2-cup
3-cup
4-cup
ra Arabian Seab 2-NE
903–1974 n ¼ 14
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.60+ 0.81* 0.65* 0.46+ 0.43
0.59 0.43 0.55 0.26 0.62+
0.51 0.01 0.40 0.20 0.21
0.23 neg 0.13 neg neg
neg neg 0.38 neg 0.02
252
1974–3141 n ¼ 13
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.80* 0.70* 0.73* 0.47 0.11
0.75* 0.90* 0.67* 0.50+ 0.51+
0.56 0.81* 0.45 0.50 0.36
0.19 0.19 0.16 0.06 0.10
0.12 0.14 neg 0.24 0.19
137
243
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 Table 1 (Continued) Station
2-SW
3-NE
3-SW
4-NE
4-SW
Depth (m)
Ratio
ra No-shift
1-cup
2-cup
3-cup
4-cup
Settling velocity (m/day)
924–1996 n¼6
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.97* 0.90* 0.95* 0.98* 0.82
0.97* 0.83 0.99* 0.92 0.93
0.85 0.36 0.89 0.91 0.99*
0.83 0.86 0.88 0.81 0.97*
0.18 0.74 0.38 0.05 0.21
252
1996–3159 n¼7
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.95* 0.90* 0.97* 0.96* 0.96*
0.96* 0.79 0.93* 0.89 0.87
0.56 0.32 0.61 0.70 0.50
0.19 0.03 0.12 0.08 0.09
neg neg neg neg neg
274
858–1857 n ¼ 10
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.74+ 0.94* 0.84* 0.77* 0.07
0.51 0.78* 0.90* 0.88* 0.54
0.03 0.39 0.58 0.61 0.10
neg neg 0.19 0.55 0.53
neg neg neg 0.26 0.63+
235
1857–2966 n ¼ 18
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.89* 0.96* 0.68* neg neg
0.67* 0.83* 0.51 neg 0.32
0.54 0.57 0.38 neg 0.24
0.44 0.33 0.39 0.36+ 0.58+
0.02 neg 0.02 0.27 0.36
261
888–1882 n¼7
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.79 0.48 0.64 0.26 0.00
0.92* 0.77 0.75+ 0.35+ 0.38+
0.73 0.71 0.72 0.06 neg
0.39 0.82+ 0.37 neg neg
0.07 0.63 0.19 neg neg
117
1882–2991 n ¼ 10
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.56+ 0.58+ 0.78* 0.76* 0.79*
0.44 0.49 0.56 0.78* 0.50
neg 0.25 neg 0.07 0.02
neg neg neg 0.01 neg
neg neg neg 0.07 neg
261
821–2229 n ¼ 18
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.85* 0.69* 0.75* 0.65* 0.63*
0.65* 0.44 0.78* 0.52 0.58*
0.14 0.55 0.52 0.35 0.59*
0.04 0.81* 0.11 0.22 0.53
neg 0.40 neg 0.02 0.30
331
2229–3478 n ¼ 18
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.73* 0.77* 0.74* 0.83* 0.86*
0.86* 0.49 0.72* 0.75* 0.90*
0.35 0.28 0.36 0.56 0.68*
0.17 0.06 neg 0.41 0.49
0.08 neg neg 0.34 0.34
294
2215–3489 n ¼ 11
Corg/Al Ca/Al Si/Al
0.26 0.85* 0.70*
0.66+ 0.36 0.89*
0.23 0.06 0.91*
0.40 neg 0.15
0.33 neg neg
300
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Table 1 (Continued) Station
5
Depth (m)
Ratio
ra No-shift
1-cup
2-cup
3-cup
4-cup
Settling velocity (m/day)
Si/Corg Si/Ca
0.78* 0.72*
0.92* 0.70
0.81* 0.71
0.10 0.17
neg neg
800–2363 n¼7
Corg/Al Ca/Al Si/Al
0.43 0.36 0.56
0.49+ 0.62+ 0.56+
neg 0.33 neg
neg neg neg
neg 0.24 neg
92
2363–3915 n ¼ 16
Corg/Al Ca/Al Si/Al Si/Corg Si/Ca
0.71* 0.61* 0.38 0.68* 0.69*
0.58 0.11 0.52+ 0.81* 0.52
0.59 0.14 0.23 0.52 0.65
0.44 neg 0.19 0.31 0.23
0.52 neg 0.08 0.15 0.18
183
a Asterisks indicate the correlation coefficient which are significant at the a ¼ 0:02 level. +sign indicates correlations which are not significant at this level, but are the best correlation. ‘neg’ indicates regressions that show a negative correlation. Ca represents CaCO3, all ratios were molar ratios. n is the average number of points in the regression. b Cup rotations represent 8.5 day cycles except at Station 5 where rotations occur every 17 days. NE and SW denote Monsoonal season.
trap was raining to the deeper trap over a long time lag, many trap cup rotations (up to 9) were used in the regression analysis. The data reported in Table 1 represent the best positive correlations found. Settling velocities were determined by considering the spacing between traps and the time lag, or number of cup-shifts, necessary to yield the best linear correlation. The best linear correlation was determined using the following criteria. The number of cup rotations for which there were the most statistically significant correlation coefficients was deemed the most important criterion. The number of best fit correlation coefficients was deemed the second most important criterion. Thus, for EqPac, 51S, only the no-shift regression yielded statistically significant correlations. At the equator (2284–3618 m), several cup rotation scenarios yielded statistically significant correlations, but the no-shift option yielded the most significant correlations. In the AS-5 (800–2363 m) trap pair, no statistically significant correlation coefficient was found, yet for all element ratios, the best linear regression was found when the lower trap was subjected to a one-cup rotation. A final example is given by the AS-2 trap pair (NEM, 1974–3141 m). The same number of significant correlations are found for the no-shift and one-shift regressions. No significant correlations are found in analyzing the Si/Corg and Si/Ca ratios, but for these two cases, the one cup-shift regression yields a better correlation coefficient than do the other options. In this example, a onecup time lag is deemed the most appropriate for determination of settling velocity. If, as was stated earlier, particles with different compositions settled at quite different rates, there would be systematic offsets between the number of cup rotations necessary to optimize the correlation coefficient for different element ratios. Such a systematic difference was not observed. In most cases, the optimal regression coefficient for one elemental ratio is the same for other element ratios. When the best linear regression fit predicts a different cup-shift for one ratio
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
245
compared to another ratio, the entire series of ratios is evaluated and the offset most often represented by the statistically significant fit was chosen. Defining the number of cup rotations necessary to optimize the regression coefficient for a number of element ratios does not, in and of itself, define the time lag between particles arriving at the upper and lower traps. At the EqPac sites, when a one-cup shift results in the best set of ‘r’ values, it is assumed that this represents a 17-day offset between material reaching the upper trap and material reaching the deeper trap. Thus the predicted settling velocity for particles falling at the Equator between 880 and 2284 m is 1404/17=83 m/day. In many cases, the no-shift option presents the best set of correlation coefficients. Under this condition, the settling velocity may be infinitely fast. However, if settling were extremely fast, the no-shift regression would be the best fit for traps located at all depths. The very fast settling hypothesis was tested by determining the best fit regression between the shallowest and deepest traps. At all sites where two sets of traps indicate rapid particle settling (no-shift regression), this analysis showed that settling between the upper-most and lower-most traps required a one-cup rotation. This means that settling velocity between the shallowest and deepest traps is constrained; in the case of 51N at EqPac, the predicted velocity between 1191 and 3793 m would be 2602 m/ 17day=153 m/day. This, however, does not, imply that particles settling between 1191 and 2091 m and particles settling between 2091 and 3793 m are all falling at this rate. In fact, this could not be the case because the spacing between trap pairs requires that particles fall faster than 153 m/day between the deeper traps and slower than 153 m/day in the shallower traps. An assumption was made that the travel time was 8.5 days for the 900 m between the upper traps and 8.5 days for the 1702 m between the lower traps. This is consistent with a one-cup (17-day) offset between the shallowest and deepest cups. There is room in this analysis for velocities to be greater than estimated, as a one-cup rotation implies an average offset of 17 days but may actually represent a o17-day offset. Choosing the cup cycle length as the lag time when the regression analysis suggests a one-cup offset is the most reasonable estimate of average lag time. When there is no offset between cups in adjacent traps, the most reasonable assumption is that the lag is 0.5*cup cycle. This probably establishes a lower estimate of particle settling rate, but this approach yields results consistent with velocities established for particles falling from the shallowest to the deepest trap.
3. Discussion The range of settling velocities established by this method is similar for both the EqPac and AS regions; 80–270 m/day and 90–330 m/day, respectively (Fig. 3). Both pairs of traps from EqPac, from the equator and 51N, indicate an increase in settling velocity between B1000–2200 m and B2200–3500 m. The trap pair at 121S spans 1200–3500 m and suggests a settling velocity of >250 m/d. Although there is considerable uncertainty in the quantification of particle settling rate using the procedure outlined above, the limited data from EqPac suggest that particle settling rates in the deep ocean increase by B60%. Traps from the Arabian Sea show a similar pattern whereby most trap pairs indicate greater settling velocities at depths >2000 m compared to traps located at 800–2000 m. This pattern is not without exceptions, yet most trap pairs (4 out of 6) indicate an increase in particle settling rate
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Settling Velocity (m/d) 0
50
100
150
200
250
300
350
0 Equatorial Pacific 500 EQ
1000
5 S
12 S
Depth (m)
1500 5 N
2000 5 N
2500
3000
3500
a
EQ
4000
Settling Velocity (m/d) 0
50
100
150
200
250
300
350
0 Arabian Sea 500
1000
Depth (m)
1500
2000
2500
3000
Sta. 2 Sta. 3
3500
Sta. 4 Sta. 5
b
4000
Fig. 3. (a) Particle settling velocities for EqPac traps. (b) Particle settling velocities for traps in the AS. Open symbols designate traps deployed during the NE Monsoon period, solid symbols denote traps deployed during the SW Monsoon period. The traps at Sta. 5 were operating over both periods.
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
with depth. The velocities obtained from the AS analyses are generally greater than the velocities obtained from the analysis of EqPac data, but this is expected given that the cup cycles were 17 days for EqPac and 8.5 days for AS (Sta. 2–4). The greater frequency of cup cycling permits greater resolution of faster settling particles. The AS trap data were divided into deployments during the NE Monsoon period (Nov. 94– April 95) and SW Monsoon period (May 95–Oct. 95). There was no systematic difference in settling velocities between these periods even though the SW Monsoon particle rain contained a greater fraction (wt%) and a greater mass flux of lithogenic particles (Honjo et al., 1999). This leads to the hypotheses that (1) monsoonal forcing and (2) the presence of lithogenic ballast do not influence particle settling rates. This second point is perhaps best illustrated by comparing AS5 to EqPac stations at 51N and 51 S. The traps at AS-5 rotated on a 17-day cycle, like the EqPac traps, and the velocities obtained for this trap pair are identical to the velocities obtained at the 51 N and 51S sites. Although mass rain rates at these two sites were similar (Table 2), the AS-5 station had a greater fraction of lithogenic material compared to the EqPac stations. This adds further support to the hypothesis that ballasting by lithogenic particles does not control particle settling velocity. Traps from the EqPac region did show a systematic difference in settling rate with surface ocean forcing (Fig. 4). For this analysis, the EqPac trap data were divided mid-year in 1992, the first half corresponding to El Nino conditions and the second half representative of non-El Nino conditions (Kessler and McPhaden, 1995). Higher settling velocities, by a factor of 2, were recorded during the El Nino phase of forcing at 1401W. Also, it appears that stations off the equator have greater settling velocities than stations near the equator. This seems counterintuitive, as one might expect regions or sites of greater particle production (e.g. near the equator and during non-El Nino periods) to yield particles that sink at greater rates. It appears that primary productivity is negatively correlated with particle settling velocity, although more data and longer trap records would help confirm this surprising result.
Table 2 Properties of sediment traps and constituents. Flux and wt% values represent annual averages for the deepest two sediment traps Station ID
Cup cycle (day)
Mass flux (mg/m2day)
wt% Corg
CaCO3
Bio SiO2
Other
Equatorial Pacific Equator 51N 51S 121S
17 17 17 17
100 75 65 25
4.5 5.3 4.2 3.4
66 66 69 78
25 22 23 13
5 7 4 5
Arabian Sea Sta. 2 Sta. 3 Sta. 4 Sta. 5
8.5 8.5 8.5 17
234 220 163 62
6.6 6.6 6.1 5.7
50 48 51 61
19 19 18 11
24 26 25 22
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Approx.Settling Velocity (m/day)
200 El Nino Non-El Nino 150
100
50
0 Eq. (880-2284)
Eq. (2284-3618)
5˚N (1191-2091)
Location (depth m)
Fig. 4. Settling velocities for particles as derived from sediment trap analyses by dividing the EqPac trap deployment period into one half a year (Jan–Aug, 1992), considered to be influenced by El Nino equatorial conditions, and one half a year (Aug–Dec, 1992) considered to be dominated by forcing that represented non-El Nino conditions.
The difference in settling velocity between shallow and deep traps is not as apparent in the AS data as in the EqPac data; Fig. 5 provides a comparison of both sites with all traps averaged. The pattern of a greater velocity gradient at EqPac relative to AS is interesting in light of the difference in wt% organic carbon (Corg) between these sites. Material falling through the ocean interior at the Arabian Sea generally contains a greater fraction of Corg than material raining to the sea floor in the Equatorial Pacific (Table 2). In a paper that also appears in this volume, Armstrong et al. (2002) have shown that at EqPac there is a steeper gradient of wt%Corg with depth than there is at the AS sites. More Corg is lost from particles falling between 1000 and 3500 m at EqPac than is lost from particles falling through the same depths at the AS. The steeper velocity gradient at EqPac relative to the gradient at the AS sites may be related to this pattern of Corg loss, but it is uncertain at this time whether settling velocity affects Corg content or Corg content affects settling velocity. The general picture is obscured in a detailed image of Corg and settling velocity (Fig. 6). Although the trend in all trap analyses is for a loss of Corg with depth, the traps that exhibit the greatest change in organic carbon content do not always exhibit an increase in settling velocity. An increase in particle settling rate may be caused by several different mechanisms; e.g., all particles may sink at the same speed but are intercepted less frequently with depth. Particles may be repackaged at depth and become denser. Previous studies have noted changes in classes of fecal pellets present at different depths (Honjo, 1978; Urrere and Knauer, 1981), suggesting reworking of sinking material by deep-living heterotrophs (Karl et al., 1988, Wishner et al., 1998). Alternatively, particles may grow in size as they sink, thereby increasing the sinking rate. According to calibrations of particle size and settling velocity (Alldredge and Gotschalk, 1988), a change in particle settling velocity from 100 to 200 m/day would correspond with an increase in particle diameter of about an order of magnitude, from B10 to B100 mm. Another mechanism might involve the loss of the slowest sinking particles from the spectrum of slow and fast sinking
W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251
249
Settling Velocity (m/d) 0
100
50
150
200
250
300
350
0
500
Depth range (m)
1000
? ?
1500
2000
2500
3000
3500
Arabian Sea EqPac
4000
Fig. 5. The average settling velocity for upper to mid-level and mid-level to deep traps in the AS and EqPac regions. Vertical range denotes depth range of traps. Horizontal bar denotes the s.e. of the mean for 3 shallow and 2 deep trap pairs at EqPac and 6 shallow and 7 deep trap pairs for the AS. The lines in the upper left indicate the particle settling velocities determined by Pilskaln et al. (1998) for Central California coastal water between 100–500 m and the range in velocities reported by Pilskaln et al. (1998) by workers who have looked at particle settling rates in the surface (upper 40 m) ocean. There is no certainty that the settling velocities determined for coastal settings is similar to settling velocities in the EqPac and AS regions, thus the dotted lines are drawn with a question mark.
particles. If this latter mechanism were responsible, the distribution of particles in the deep ocean would reflect the profile of settling velocity. The conclusion of this study is that particle settling rates increase with depth in the ocean in a systematic fashion (Fig. 5). The strength of this conclusion is drawn from the analyses and data presented by Pilskaln et al. (1998) in which particle settling rates were analyzed at a continental margin site for depths 100–500 m. They found mean rates varied between 16 and 25 m/day and argue that other estimates of particle sinking rates in shallow water (o500 m) may be biased by observation of mostly fast sinking particles. Thus, the trend line shown in Fig. 5 expresses a dramatic change in particle sinking rate between the upper and deep ocean. Such a pattern, and whether it has a linear or curvilinear functionality, will be relevant and important in the formulation of GCM’s in which particle remineralization rates within the water column must be parameterized. For example, the ocean becomes more corrosive to CaCO3 as a function of depth; thus one would expect particulate carbonate to dissolve more readily as it sinks deeper. Yet an analysis of trap data shows that the wt% CaCO3 lost during transit from 1000 to 2000 m is similar to or more than the wt% lost during transit from 2000 to 3500 m (Tsunogai and Noriki, 1991). In
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W.M. Berelson / Deep-Sea Research II 49 (2002) 237–251 Arabian Sea Stations 2-4 400 350
Sta. 4
Settling Velocity (m/day)
300 250
Sta. 3
Sta. 2
200 Sta. 3
150
Sta. 2
100 50 0 5
6
7 8 Average wt. % Corg
9
10
Fig. 6. Settling velocity and average wt% organic carbon content of trap material for Arabian Sea Stations 2, 3 and 4. The points with bullseyes represent traps deployed during the SW Monsoonal period, other points indicate deployment during the NE Monsoonal period. Arrows indicate the upper and lower trap in a sequence. All trap pairs show a decline in %Corg from the shallower to deeper trap, as all arrows move to the left in the x-axis domain. In some cases a small change in wt% Corg corresponds with a large change in settling velocity (Sta. 3 SW Monsoon) in other traps a large change in wt% Corg has a small effect on settling rate (Sta. 3 NE Monsoon).
EqPac and AS traps, carbonate rain decreases by 8.9 wt%/km between 1000 and 2000 m and by 6.5 wt%/km between 2000 and 3500 m. An increase in particle settling rate can explain this apparent inconsistency. Acknowledgements I acknowledge the high quality of the trap data and thank S. Honjo, J. Dymond and R. Collier for making this data available. Thanks to D. Hammond, T. Michaels and two anonymous reviewers for their helpful comments. This is US JGOFS contribution number 694. References Alldredge, A.L., Gotschalk, C., 1988. In situ settling behavior of marine snow. Limnology and Oceanography 33, 339–351. Alldredge, A.L., Gotschalk, C., 1989. Direct observations of he mass flocculation of diatom blooms: characteristics, settling velocities and formation of diatom aggregates. Deep-Sea Research 36, 159–171. Armstrong, R.A., Lee, C., Hedges, J.I., Honjo, S., Wakeham, S.G., 2002. A new mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast mineral. Deep-Sea Research II 49, 219–236.
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