Controls on the remineralization depth profile of ...ieng6.ucsd.edu/~aruacho/ASLO_2011.pdfand...
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−160 −140 −120 −100 −80 −60 −40 −80 −60 −40 −20 0 Degrees Longitude Degrees Latitude Pacific Transect distance (km) depth (m) 0 1000 2000 3000 4000 5000 6000 7000 −1000 −900 −800 −700 −600 −500 −400 −300 −200 −100 0 1 2 3 4 5 6 7 Introduction Carbon is transported to depth in the ocean by sinking particles of organic matter. The biological pump is made up of four key processes (1) the uptake of carbon and nutrients in the upper ocean by plankton during photosynthesis, (2) the sinking of particulate organic matter, (3) the remineralization of organic carbon and nutrients leading to an attenuation of particle fluxes with depth, and (4) the transport of inorganic carbon and nutrients back to the surface ocean. The deeper particulate carbon fluxes penetrate in the water column before being remineralized the longer the carbon remains in the ocean before being transported back to the surface ocean where it can escape to the atmosphere. The timescale for carbon sequestration can be anywhere from tens of years to thousand of years depending on the penetration depth of the particulate fluxes. Controls on the remineralization depth profile of particulate organic carbon in the ocean Poster ID: 11 Angel Ruacho 1 , Francois Primeau 1 , Lionel Guidi 2 , and Lars Stemmann 3 1. Department of Earth System Science, University of California, Irvine, USA 2. Department of Oceanography, University of Hawai’i, USA 3. Universite Pierre et Marie Curie and Laboratoire d’Oceanographie de Villefranche, Paris, France Data The data was collected through the use of an underwater video recorder, which can take images of macrozooplankton and particulate matter from a few centimeters to as small as 50 microns in size. It records images by having a camera mounted in one arm and focus on a point midway of the two arms while the second arm has a strobe that illuminates the imaged volume. It is useful because distribution patterns of macrozooplankton can quickly be measured without having to use bottles or nets which can damage the plankton. The data was collected from various locations in the Pacific Ocean, starting at the surface and recording an image every 5 meters. The images are recorded digitally and then processed by an image analysis software to assign a projected area to each particle (Guidi et al. 2009). In order to collect data the UVP is lowered into the water on a cable at a speed so that there is no overlapping regions in the images. The images from the UVP can be used to derive the flux of particulate organic carbon, POC, (Stemmann et al. 2008). Here we focus on a transect in the Pacific Ocean, figure 1 (a), where the data has been contoured, figure 1 (b). Goal Our goal is to formulate an improved parameterization for the attenuation of particulate organic carbon (POC) fluxes with depth that can be used in Earth system models that seek to simulate the carbon cycle in order to simulate the future climate of the planet. To this end we are performing a statistical analysis of data on the concentration and size of particles in the ocean collected using an underwater video recorder. Figure 1: Schematic diagram of the biological carbon pump. (Image generated by Hannes Grobe Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany.) 0 1000 2000 3000 4000 5000 6000 7000 8000 −0.5 0 0.5 1 1.5 Distance (Km) b Values b Value Comparisons b value estimated for each station b value varying with oxygen (a) (b) Figure 3: (a) Station locations for the South Pacific BIOSOPE cruise. (b) Contour plot of the natural logarithm of the flux of carbon (mg C m -2 d -1 ) as a function of depth and distance along the cruise track. Conclusion: • The parameters of the Martin curve are easy to estimate using ordinary least squares applied to the log transformed fluxes. • The parameters of the Lutz model are difficult to estimate in the sense that the nonlinear least squares does not always converge. However, we have implemented a robust Monte-Carlo estimation procedure based on the nested sampling method described in Sivia and Skilling (2008). • Lutz curve provides a better fit to the data compared to the Martin curve but this is at the expense of increasing the number of free parameters in the model. Preliminary results from the Bayesian model comparison analysis of the Martin curve versus the Lutz curve suggests that the Martin curve provides a more probable (and parsimonious) description of the POC flux data set, but the results are sensitive to the subsets of stations we include in the analysis and for the prior range for the parameters. Because we have not yet analyzed the full UVP data set we cannot draw any firm conclusions yet. • The analysis of the model in which the attenuation of the POC flux depends on the in-situ oxygen concentration in the upper 500 m does not provide a significantly better fit than one in which the attenuation is constant along the transect. • In future work we will test other environmental variables and analyze the full data set. Models: To describe the attenuation of the POC flux with depth we tested two parametric functional forms - the so-called Martin curve and what we call the Lutz curve. 0 200 400 600 800 1000 1200 −3500 −3000 −2500 −2000 −1500 −1000 −500 0 Flux mg C m −2 d −1 Depth (m) Martin Curve with Varying b J(z)= Jo × z zo -b Flux mg C m −2 d −1 b =0.96 b =0.72 b =0.40 J(z)= Ja + dJo × exp -k z - zo zo 0 50 100 150 200 250 300 !3500 !3000 !2500 !2000 !1500 !1000 !500 0 Depth (m) Lutz Curve with Varying Ja Flux mg C m −2 d −1 Ja =3.32 Ja =9.03 Ja = 24.53 Figure 2: (A) UVP in its frame, (B) UVP mounted on a 24-place Niskin bottle rosette frame, (C) schematic diagram of the Underwater Vision Profiler light system and illuminated volume of water (in pink). The recorded image is one portion of this zone (as drawn in 1B). Method We used the least squares method and a Monte Carlo sampling method to estimate the parameters of various parametric models to describe the attenuation of the flux of particulate organic carbon as a function of depth. We tested two function forms for the parametric model - the so-called Martin curve and what we call the Lutz curve (see next panel). In the context of these parametric functional form we tried • a model in which the rate of attenuation varies independently at each station • a model in which the rate of attenuation is a constant for all stations • a model in which the rate of attenuation is a function of the averaged in-situ oxygen concentration in the upper ocean. We are planing to test several other models in which the attenuation of particle fluxes depends on other environmental variables. We are also developing a Bayesian model comparison method to select the model that best fits the data with a minimum number of adjustable parameter. References: Guidi, L., Stemmann, L., Jackson, G.A., Ibanez, F., Claustre, H., Legendre, L., Picheral, M., and Gorsky, G., 2009: Effects of phytoplankton community on production, size and export of large aggregates: A world-ocean analysis. Limno. Oceanogr., 54(6), 1951-1963 Stemmann, L., Guidi, L.,Youngbluth, M., Robert K., Hosia, A., Picheral, M., Paterson, H., Ibanez, F., Lombard, F., and Gorsky, G, 2008: Global zoogeography of fragile macrozooplankton in the upper 100-1000 m inferred from the underwater video profiler. ICES J. of Mar Sci, 65(3), 433-442, doi: 10.1093/icesjms/fsn010. Sivia, D.S., and Skilling J. 2008: Data Analysis A Bayesian Tutorial Second Edition. Oxford University Press. 246pp. distance (km) depth (m) 0 1000 2000 3000 4000 5000 6000 7000 −1000 −900 −800 −700 −600 −500 −400 −300 −200 −100 1 2 3 4 5 6 7 Figure 4: Parametric functional forms for the flux of POC as a function of depth. The left panel shows the Martin curve and the right panel shows the Lutz curve. Figure 4: Contour plots of the modeled natural logarithm of the POC flux (mg C m -2 d -1 ) as a function of depth and distance along the cruise track. The left panels correspond to the Martin curve and the right panels correspond to the Lutz curve. The Lutz model tends provides a better fit in the upper ocean, but also seems to underestimate the flux attenuation along the middle of the cruise track where export fluxes are low. The Martin curve with a constant value of b tends to underestimate the POC flux penetration depth at the beginning and end of the cruise track where export fluxes are large and to overestimate the fluxes in the center of the cruise track where export fluxes are smaller. Martin curve with b-values that vary independently along the cruise track distance (km) depth (m) 0 1000 2000 3000 4000 5000 6000 7000 −1000 −900 −800 −700 −600 −500 −400 −300 −200 −100 0 1 2 3 4 5 6 7 Martin curve with a constant b-value along the cruise track Figure 5: (a) Contour plot of the in-situ oxygen concentration in ml/L along the cruise track in the depth range between 100 m and 500 m. (b) Plot of ZO2, a standardized variable for the variations of the averaged oxygen concentration in the depth range between 100 m and 500 m along the cruise track. Models in which the POC flux attenuation is a function of environmental variables: example using the in-situ oxygen concentration To describe the attenuation of the POC flux with depth we tested two parametric functional forms - the so-called Martin curve and what we call the Lutz curve. distance (km) depth (m) 0 1000 2000 3000 4000 5000 6000 7000 −500 −400 −300 −200 −100 0 2 4 0 1000 2000 3000 4000 5000 6000 7000 −2 −1 0 1 2 distance (km) Z O2 (a) (b) distance (km) depth (m) 0 1000 2000 3000 4000 5000 6000 7000 −1000 −900 −800 −700 −600 −500 −400 −300 −200 −100 0 1 2 3 4 5 6 7 Martin curve with a b-value that is a linear function of ZO2 Figure 6: (a) Contour plots of the modeled natural logarithm of the POC flux (mg C m -2 d -1 ) as a function of depth and distance along the cruise track for the case where the b-value is a function of in-situ oxygen concentration. The fit does not improve much compared with the model with a constant b-value. (b) Logarithm of the fitted export flux along the cruise track. Most of the variations in ZO2 are associated with variations in the export flux, Jo. bo =0.5351 ± 0.0101 m = -0.0035 ± 0.0036 b = b o + mZ O2 0 1000 2000 3000 4000 5000 6000 7000 2 4 6 distance (km) log J o Figure 7: Plot of the optimal b-values for the model in which they are free to vary at each station along the cruise track (blue circles) and for the model in which they vary as a function of oxygen (red circles). Oxygen does not appear to be an environmental variable that controls the attenuation of POC flux with depth. J (z) = J o × ( z/z o ) -b b = b o + mZ O2 Z O2 = O 2 -O 2 σ O2 O2: oxygen concentration averaged between 100 m and 400 m O2: mean of O2 along the cruise track σO2 : standard deviation of O2 along the cruise track distance (km) depth (m) 0 1000 2000 3000 4000 5000 6000 7000 −1000 −900 −800 −700 −600 −500 −400 −300 −200 −100 0 1 2 3 4 5 6 7 Sunday, February 13, 2011
Transcript of Controls on the remineralization depth profile of ...ieng6.ucsd.edu/~aruacho/ASLO_2011.pdfand...
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IntroductionCarbon is transported to depth in the ocean by sinking particles of organic matter. The biological pump is made up of four key processes (1) the uptake of carbon and nutrients in the upper ocean by plankton during photosynthesis, (2) the sinking of particulate organic matter, (3) the remineralization of organic carbon and nutrients leading to an attenuation of particle fluxes with depth, and (4) the transport of inorganic carbon and nutrients back to the surface ocean. The deeper particulate carbon fluxes penetrate in the water column before being remineralized the longer the carbon remains in the ocean before being transported back to the surface ocean where it can escape to the atmosphere. The timescale for carbon sequestration can be anywhere from tens of years to thousand of years depending on the penetration depth of the particulate fluxes.
Controls on the remineralization depth profile of particulate organic carbon in the ocean Poster ID: 11Angel Ruacho1, Francois Primeau1, Lionel Guidi2, and Lars Stemmann3
1. Department of Earth System Science, University of California, Irvine, USA2. Department of Oceanography, University of Hawai’i, USA
3. Universite Pierre et Marie Curie and Laboratoire d’Oceanographie de Villefranche, Paris, France
DataThe data was collected through the use of an underwater video recorder, which can take images of macrozooplankton and particulate matter from a few centimeters to as small as 50 microns in size. It records images by having a camera mounted in one arm and focus on a point midway of the two arms while the second arm has a strobe that illuminates the imaged volume. It is useful because distribution patterns of macrozooplankton can quickly be measured without having to use bottles or nets which can damage the plankton. The data was collected from various locations in the Pacific Ocean, starting at the surface and recording an image every 5 meters. The images are recorded digitally and then processed by an image analysis software to assign a projected area to each particle (Guidi et al. 2009). In order to collect data the UVP is lowered into the water on a cable at a speed so that there is no overlapping regions in the images. The images from the UVP can be used to derive the flux of particulate organic carbon, POC, (Stemmann et al. 2008). Here we focus on a transect in the Pacific Ocean, figure 1 (a), where the data has been contoured, figure 1 (b).
GoalOur goal is to formulate an improved parameterization for the attenuation of particulate organic carbon (POC) fluxes with depth that can be used in Earth system models that seek to simulate the carbon cycle in order to simulate the future climate of the planet. To this end we are performing a statistical analysis of data on the concentration and size of particles in the ocean collected using an underwater video recorder.
Figure 1: Schematic diagram of the biological carbon pump. (Image generated by Hannes Grobe Alfred Wegener Institute for Polar and Marine Research Bremerhaven, Germany.)
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Figure 3: (a) Station locations for the South Pacific BIOSOPE cruise. (b) Contour plot of the natural logarithm of the flux of carbon (mg C m-2 d-1 ) as a function of depth and distance along the cruise track.
Conclusion:
• The parameters of the Martin curve are easy to estimate using ordinary least squares applied to the log transformed fluxes.
• The parameters of the Lutz model are difficult to estimate in the sense that the nonlinear least squares does not always converge. However, we have implemented a robust Monte-Carlo estimation procedure based on the nested sampling method described in Sivia and Skilling (2008).
• Lutz curve provides a better fit to the data compared to the Martin curve but this is at the expense of increasing the number of free parameters in the model. Preliminary results from the Bayesian model comparison analysis of the Martin curve versus the Lutz curve suggests that the Martin curve provides a more probable (and parsimonious) description of the POC flux data set, but the results are sensitive to the subsets of stations we include in the analysis and for the prior range for the parameters. Because we have not yet analyzed the full UVP data set we cannot draw any firm conclusions yet.
• The analysis of the model in which the attenuation of the POC flux depends on the in-situ oxygen concentration in the upper 500 m does not provide a significantly better fit than one in which the attenuation is constant along the transect. • In future work we will test other environmental variables and analyze the full data set.
Models:To describe the attenuation of the POC flux with depth we tested two parametric functional forms - the so-called Martin curve and what we call the Lutz curve.
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Ja = 3.32Ja = 9.03Ja = 24.53
Figure 2: (A) UVP in its frame, (B) UVP mounted on a 24-place Niskin bottle rosette frame, (C) schematic diagram of the Underwater Vision Profiler light system and illuminated volume of water (in pink). The recorded image is one portion of this zone (as drawn in 1B).
MethodWe used the least squares method and a Monte Carlo sampling method to estimate the parameters of various parametric models to describe the attenuation of the flux of particulate organic carbon as a function of depth. We tested two function forms for the parametric model - the so-called Martin curve and what we call the Lutz curve (see next panel). In the context of these parametric functional form we tried• a model in which the rate of attenuation varies independently at each station• a model in which the rate of attenuation is a constant for all stations • a model in which the rate of attenuation is a function of the averaged in-situ oxygen concentration in the upper ocean.We are planing to test several other models in which the attenuation of particle fluxes depends on other environmental variables. We are also developing a Bayesian model comparison method to select the model that best fits the data with a minimum number of adjustable parameter.
References:
Guidi, L., Stemmann, L., Jackson, G.A., Ibanez, F., Claustre, H., Legendre, L., Picheral, M., and Gorsky, G., 2009: Effects of phytoplankton community on production, size and export of large aggregates: A world-ocean analysis. Limno. Oceanogr., 54(6), 1951-1963
Stemmann, L., Guidi, L.,Youngbluth, M., Robert K., Hosia, A., Picheral, M., Paterson, H., Ibanez, F., Lombard, F., and Gorsky, G, 2008: Global zoogeography of fragile macrozooplankton in the upper 100-1000 m inferred from the underwater video profiler. ICES J. of Mar Sci, 65(3), 433-442, doi: 10.1093/icesjms/fsn010.
Sivia, D.S., and Skilling J. 2008: Data Analysis A Bayesian Tutorial Second Edition. Oxford University Press. 246pp.
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Figure 4: Parametric functional forms for the flux of POC as a function of depth. The left panel shows the Martin curve and the right panel shows the Lutz curve.
Figure 4: Contour plots of the modeled natural logarithm of the POC flux (mg C m-2 d-1 ) as a function of depth and distance along the cruise track. The left panels correspond to the Martin curve and the right panels correspond to the Lutz curve. The Lutz model tends provides a better fit in the upper ocean, but also seems to underestimate the flux attenuation along the middle of the cruise track where export fluxes are low. The Martin curve with a constant value of b tends to underestimate the POC flux penetration depth at the beginning and end of the cruise track where export fluxes are large and to overestimate the fluxes in the center of the cruise track where export fluxes are smaller.
Martin curve with b-values thatvary independently along the cruise track
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Martin curve with a constant b-value along the cruise track
Figure 5: (a) Contour plot of the in-situ oxygen concentration in ml/L along the cruise track in the depth range between 100 m and 500 m. (b) Plot of ZO2, a standardized variable for the variations of the averaged oxygen concentration in the depth range between 100 m and 500 m along the cruise track.
Models in which the POC flux attenuation is a function of environmental variables: example using the in-situ oxygen concentration
To describe the attenuation of the POC flux with depth we tested two parametric functional forms - the so-called Martin curve and what we call the Lutz curve.
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Martin curve with a b-value thatis a linear function of ZO2
Figure 6: (a) Contour plots of the modeled natural logarithm of the POC flux (mg C m-2 d-1 ) as a function of depth and distance along the cruise track for the case where the b-value is a function of in-situ oxygen concentration. The fit does not improve much compared with the model with a constant b-value. (b) Logarithm of the fitted export flux along the cruise track. Most of the variations in ZO2 are associated with variations in the export flux, Jo.
bo = 0.5351± 0.0101m = !0.0035± 0.0036
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Figure 7: Plot of the optimal b-values for the model in which they are free to vary at each station along the cruise track (blue circles) and for the model in which they vary as a function of oxygen (red circles). Oxygen does not appear to be an environmental variable that controls the attenuation of POC flux with depth.
J(z) = Jo !!z/zo)!b
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O2: oxygen concentration averaged between 100 m and 400 m!O2": mean of O2 along the cruise track!O2 : standard deviation of O2 along the cruise track
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Sunday, February 13, 2011
