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(09.12.2024)

NPP Products

CbPM Net Primary Production (NPP) calculations

The following text provides a brief description of the Carbon-based Productivity Model (CbPM) NPP calculations. Select one of the other tabs above to find similar information regarding the other NPP Products algorithms.

The CbPM was first described by Behrenfeld et al. (2005) and has recently been expanded upon by Westberry et al. (2008). CbPM products currently available through this website are based on the updated model by Westeberry.

The appropriate way to treat light penetration through the euphotic zone is to attenuate light in the model spectrally. This is one of the improvements made in the revised model by Westberry et al. (2008). However, the other chlorophyll-based model products (currently the VGPM and Eppley-VGPM) use z_eu values calculated from satellite chlorophyll (chl_sat) data using the Morel and Berthon (1989) model. This can impact intercomparisons between CbPM products and the VGPM and Eppley-VGPM.

Another important distinction to be aware of when using the CbPM NPP product or comparing them to VGPM and Eppley-VGPM data is that the two later models are based on the NASA standard chlorophyll products distributed by the Ocean Color Processing Group (http://oceancolor.gsfc.nasa.gov/), while the CbPM NPP values are based on particulate backscatter coefficients and phytoplankton absorption coefficients from the GSM spectral matching algorithm ( Garver and Siegel,1997; Maritorena et al., 2002; Siegel et al., 2002). Thus, some of the differences between NPP products are due to inconsistencies in input satellite data fields, rather than consequences of the different NPP algorithms -- although the later is generally the predominant cause of differences.

Background on the CbPM

Development of the CbPM was motivated by the long-standing failure of chlorophyll-based modeling attempts to adequately account for natural variability in chlorophyll-specific photosynthetic efficiencies -- in other words, physiology. At the simplest level, the problem is that the chlorophyll concentration we see from space is not simply a reflection of how many phytoplankton are present in a give location, but also the effect of light and nutrient conditions on the chlorophyll concentration in each phytoplankton individual. The challenge, of course, is how do you differentiate between the biomass- and physiological contributions to chlorophyll concentration, and then how do you separate the light and nutrient effects? This problem has stumped NPP modelers for over 50 years, and currently I do not see a clear path for its solution.

The CbPM concept is one that abandons the traditional approach to NPP modeling. Instead of relating NPP to chlorophyll and Pb_opt, the CbPM relates NPP to phytoplankton carbon biomass (C_phyto) and growth rate (u). The approach is made possible by two recent developments: (1) the observation that total particulate carbon concentration and C_phyto covary with light scattering properties ( Loisel et al. 2001, Stramski et al. 1999, DuRand and Olsen 1996, Green et al. 2003, Green and Sosik 2004, Behrenfeld and Boss 2003, 2006b) and (2) the construction and application of spectral matching algorithms to satellite data for simultaneously retrieving information on particulate backscattering scattering coefficients, phytoplankton pigment absorption, and colored dissolved organic carbon absorption ( Garver and Siegel,1997; Maritorena et al., 2002; Siegel et al., 2002).

CbPM relationship

With the above information on surface phytoplankton carbon and growth rates, water column integrated NPP for the original CbPM was simply:

In the updated CbPM products from the Westberry et al. model, a simple "volume function" is not employed. Rather, phytoplankton pigment concentration is allowed to vary with depth and is described as a function of depth-dependent photoacclimation and vertical variations in nutrient stress based on climatological information on vertical nutrient distributions.

Carbon

Phytoplankton carbon biomass is assessed from particulate backscattering coefficients (bbp). Details of this relationship and its derivation is provided by Behrenfeld et al. 2005. Briefly, two primary contributors to bbp are recognized, (1) a relatively stable background concentration of small scattering particles and (2) a population of scattering particles that includes phytoplankton and those constituents that covary in abundance with phytoplankton concentration. Calculating phytoplankton carbon thus simply requires subtraction of the "background" contribution and scaling the remaining bbp to phytoplankton carbon biomass. This scaling coefficient is derived by adjusting its value until the range in satellite-based phytoplankton chlorophyll-to-carbon values are comparable to the range observed in the laboratory for a wide range of growth conditions and species. The resultant relationship is:

Growth rate

Phytoplankton growth rates are derived from satellite chlorophyll-to-carbon data. The fundamental model employed is relatively straightforward:

In words, growth rate is equal to the maximum potential growth rate of a natural phytoplankton assemblage across all relevant temperatures [u(max)] corrected for the suppression of growth rate by nutrient and temperature stress [f(N,T)] and light limitation [g(Ig)].

To start, the CbPM employs a value of u(max) = 2 divisions per day, which is based on results of Banse (1991) who reviewed available field data on phytoplankton growth rates.

Next, the effects of growth irradiance [g(Ig)] are evaluated, which as mentioned above requires information on MLD, PAR, and KPAR. By evaluating the relationship between satellite g(Ig) values and chlorophyll-to-carbon values, the influence of light on pigmentation for a natural mixed phytoplankton assemblage in the absence of nutrient or temperature stress can be evaluated (see Behrenfeld et al. 2005 for details). The nature of this relationship is high chlorophyll-to-carbon values at low Ig decreasing to a minimum value at high Ig. The enhancement of chlorophyll (thus light absorption) at low light minimizes the effect of reduced light levels, but it is quite insufficient prevent any changes in growth. Light limitation of u can be calculated from the difference increases in light absorption due to photoacclimation and the 1/Ig ("one-over-light") curve. For the original CbPM satellite-based parameterization, this relationship is:

and in the updated CbPM this becomes

Nutrient and temperature effects are assumed in the original CbPM to decrease phytoplankton chlorophyll concentrations in a manner directly proportion their effect on growth rate. For each observation, the chlorophyll-to-carbon ratio under nutrient replete conditions (Chl:C_max) is evaluated from the calculated value of Ig. The ratio of satellite-derived chlorophyll-to-carbon (Chl:C_obs) and Chl:C_max provides the growth rate correction for nutrient and temperature stress:

Parameterization of this relationship using available satellite Chl:C and Ig data yields:

Please see Behrenfeld et al. (2005) for details. An important modification to the CbPM described in Westberry et al. (2008) is the inclusion of an intercept in the relationship between Chl:C and u. Specifically, as growth rate goes to zero, chlorophyll:C remains finite.

Nitrocline depth

The original cbpm did not use the nitrocline depth (zno3), but the depth integrated version by Westberry does. The Chl : C ratio is modified by how much the depth of interest exceeds zno3. Climatological values are used for the nitrolcine estimate at this time.

Variables needed

The CbPM equation is:

and is integrated down to a depth of 200 meters.

NPP calculations with the CbPM require the following input data fields:

• chl
• bbp
• par
• k490
• mld
• zno3
• day length

These ancillary data can be accessed here.

For details on CbPM implementation, please see the code.