the Creative Commons Attribution 4.0 License.

the Creative Commons Attribution 4.0 License.

# A discrete interaction numerical model for coagulation and fragmentation of marine detritic particulate matter (Coagfrag v.1)

### Gwenaëlle Gremion

### Louis-Philippe Nadeau

### Christiane Dufresne

### Irene R. Schloss

### Philippe Archambault

### Dany Dumont

A simplified model, representing the dynamics of marine organic particles in a given size range experiencing coagulation and fragmentation reactions, is developed. The framework is based on a discrete size spectrum on which reactions act to exchange properties between different particle sizes. The reactions are prescribed according to triplet interactions. Coagulation combines two particle sizes to yield a third one, while fragmentation breaks a given particle size into two (i.e. the inverse of the coagulation reaction). The complete set of reactions is given by all the permutations of two particle sizes associated with a third one. Since, by design, some reactions yield particle sizes that are outside the resolved size range of the spectrum, a closure is developed to take into account this unresolved range and satisfy global constraints such as mass conservation. In order to minimize the number of tracers required to apply this model to an ocean general circulation model, focus is placed on the robustness of the model to the particle size resolution. Thus, numerical experiments were designed to study the dependence of the results on (i) the number of particle size bins used to discretize a given size range (i.e. the resolution) and (ii) the type of discretization (i.e. linear vs. nonlinear). The results demonstrate that in a linearly size-discretized configuration, the model is independent of the resolution. However, important biases are observed in a nonlinear discretization. A first attempt to mitigate the effect of nonlinearity of the size spectrum is then presented and shows significant improvement in reducing the observed biases.

The biological carbon pump is responsible for a significant fraction of the organic carbon exports from the surface to the deep ocean (Passow and Carlson, 2012; Le Moigne, 2019), thereby influencing the climate (Kiørboe and Thygesen, 2001). Questions regarding the quantification and prediction of its efficiency and response times are still broadly unanswered. The carbon pump yields a wide variety of processes, involving the co-action of a large number of physical, chemical and biological variables (Denman et al., 2007). Coupled ocean general circulation models (OGCMs) and biogeochemical models (BGCs) contribute to our understanding of the relative importance of these processes. They were developed for decades to assess the biological pump dynamics at the global scale (e.g. Palmer and Totterdell, 2001; Aumont et al., 2003; Dutkiewicz et al., 2005) or for specific regions of the world (e.g. Sarmiento et al., 1993; Blackford and Radford, 1995; Doney et al., 2002; Wiggert et al., 2006; Karakaş et al., 2009).

Since the pioneering work of Riley (1946), BGCs have been widely used in oceanography and their complexity never ceased to increase (Vichi et al., 2007; Anderson and Gentleman, 2012). As reported by Leles et al. (2016), they evolved from a nutrient–phytoplankton–zooplankton–detritus (NPZD) type (e.g. Palmer and Totterdell, 2001), where multiple species and types of organic and inorganic constituents of the pelagic system are gathered into a broadly defined trophic compartment (Doney et al., 2003), to food webs of multiple plankton functional types (PFTs) (Hood et al., 2006; Anderson et al., 2010). In most coupled OGCMs-BGCs, efforts have concentrated on achieving an accurate representation of primary- and secondary-producer-related particle dynamics, while the detritic compartment is generally reduced to just one variable. The detritus particles found in the ocean are nevertheless essential in the downward export of carbon (Hill, 1992; Kriest, 2002). Considering only one detritus variable may lead to important biases in carbon flux estimations. The size diversity of marine particles is wide, ranging from large, rapidly sinking particulate material (i.e. marine snow) to small suspended particles and relatively non-labile dissolved organic matter (i.e. colloids), which usually also show the highest abundances. Then, their representation through a unique variable and mean values of its descriptive parameters such as sinking velocity (Doney et al., 1996; Lima et al., 2002; Aumont et al., 2003; Dutkiewicz et al., 2005; Kishi et al., 2007) to cover the entire size range in BGCs is questionable.

To overcome this caveat and improve carbon export assessments, the number of detritus-related variables in BGC models can be increased to better represent the diversity of the sinking particulate matter. For example, some studies such as Moore et al. (2002); Wiggert et al. (2006); Yool et al. (2011); Butenschön et al. (2016); Kearney et al. (2020) defined multiple (two or more) detritic compartments, each being connected differently with other variables and having constant settling velocities. This approach can certainly increase the level of realism, as these parameterizations are made based on field or experimental evidence (Doney et al., 2003), but we see two major problems with them. First, the description in the numerical framework of a high number of state variables enhances BGC models' complexity and augments the number of parameters required to characterize relations among those variables (Denman, 2003). Ultimately, it makes it challenging to properly couple them to OGCMs. In atmospheric microphysical modelling, where size spectral frameworks are used to represent the formation of clouds, efforts are made to use a small number of variables in 3-D simulations to prevent a drastic increase of the computational costs, to the expense of accuracy (Khain et al., 2015). The desire to improve realism and accuracy by adding complexity needs to be tempered by our ability to parameterize key processes as a compromise regarding computational costs and efficiency (Raick et al., 2006). As underlined by Anderson (2005), the conception of meaningful state variables and constants in numerical models is crucial, and determining representative values for parameters can be challenging (Flynn, 2005; Le Quéré, 2006). Second, this approach does not account for processes by which the size, and consequently the settling velocities of these particles, can be altered with depth. Indeed, parameterization is generally achieved by constraining detritus state variables by constant parameters or depth-dependant functions (Gloege et al., 2017, e.g. exponential decay, Martin's curve, ballast hypothesis) to represent the actions of these processes and associated living organisms. As an example, coagulation (i.e. the formation of larger particles from the collision and aggregation of smaller particles) increases particle size and may end up in aggregate formation. Fragmentation is the opposite process, breaking particles into smaller pieces. Both processes are then affecting particle size distribution but are barely explicitly implemented or parameterized in OGCMs-BGCs.

It is indeed based on the seminal work of Gelbard et al. (1980) on the sectional representation of aerosol size distribution evolution due to collision and coagulation events, that the first coagulation models applied to marine snow emerged. Jackson and Burd (1998) extended the model of Gelbard et al. (1980) and applied it to the marine environment, pointing out the role of fragmentation to counterbalance the importance of coagulation (Jackson et al., 1995; Hill, 1996). We note that coagulation and fragmentation of particles is a natural phenomenon that happens in a very broad variety of situations. It has been studied in many disciplinary fields, including atmospheric sciences, especially in microdroplet and cluster formation (e.g. Pruppacher and Klett, 2010), but also in chemistry (e.g. Lee et al., 2018), astrophysics and engineering (see the review of Pego, 2007). In oceanography, it is also applied to aggregation of nanoplastics with colloids (Oriekhova and Stoll, 2018) or the oil–marine snow interaction (Dissanayake et al., 2018; Burd et al., 2020). We note also that there a variety of modelling approaches, including Lagrangian formulations with stochastic processes (e.g. Jokulsdottir and Archer, 2016). However, Eulerian formulations are those that are still best applicable to OGCMs-BGCs and that will be considered more specifically here.

The present work stems from numerous Eulerian modelling studies, most of which are listed by De La Rocha and Passow (2007) and Jackson and Burd (2015) (e.g. for coagulation, refer to Jackson, 1990; Kriest and Evans, 1999; Jackson, 2001; Kriest, 2002, and for fragmentation to Alldredge et al., 1990; Dilling and Alldredge, 2000; Kiørboe, 2000; Ploug and Grossart, 2000; Goldthwait et al., 2004; Stemmann et al., 2004) but these models are scarcely coupled to OGCMs. It is probably due to the remaining quantitative unknowns regarding the ensemble of processes affecting transport efficiency of the particulate organic matter to depth by constraining particle size distribution even after decades of extensive work (Le Quéré et al., 2005; De La Rocha and Passow, 2007). The intrinsic heterogeneous nature of these processes at all spatiotemporal scales increases the challenge to properly implement them in complex OGCMs-BGCs and to evaluate and predict the ocean's role in the Earth's carbon budget. Similar challenges arise in atmospheric GCMs. According to Kang et al. (2019), GCMs with full cloud microphysics are still at an early stage in terms of understanding and simulating many observed aspects of weather and climate, and research is needed to circumvent these difficulties.

In order to circumvent the issues related to the representation of the dynamics of the complete particles size spectrum for OGCMs, we develop in this study a new numerical framework where a particle's size range is discretized in size bins, and where concentration dynamics over these bins is driven by coagulation and fragmentation reactions. This framework is designed to conserve mass over the size range and reactions, and can accommodate size and mass linear and nonlinear discretizations. Our approach differs from the so-called sectional approach of Jackson and Burd (2015) in that the discretized size spectrum we use is an integral quantity that depends on the discretization, as opposed to a spectral density.

Since the main motivation for developing such a model is to provide a tool allowing to characterize detritic variables relations and dynamics in coupled OGCMs without unreasonably increasing the computational cost, a formulation is sought that will attenuate the dependence of the results to the size discretization resolution. Numerical experiments are designed to study the dependence of the results on (i) the number of size bins used to discretize a given size range (i.e. the resolution) and (ii) the type of discretization (i.e. linear vs. nonlinear). Innovations of the approach with regard to previously developed coagulation–fragmentation models and designed detritic state variables are briefly discussed as well as the potential for further improvements to allow the inclusion of the presented model into OGCMs to better estimate current carbon export.

The outline of the paper is as follows. Section 2 presents the model description, Sect. 3 describes numerical experiments, and Sect. 4 presents and briefly discusses the results. A summary of our main conclusions is given in Sect. 5.

## 2.1 Discrete size spectrum

Whereas most laboratory and field studies estimate particle number concentration, *n* (m^{−3}), along a size spectrum (McCave, 1984; Jackson et al., 1995), OGCMs use tracers' (or elements') concentration, *C* (mmol m^{−3}) (e.g. carbon or nitrogen), to study fluxes among the model
compartments (Doney et al., 1996). These two variables are linked by

where 𝒩 is the particles' content of the chosen currency (mmol).

Considering a closed system in a given water volume with no particle sources or sinks, particles may be sorted over a size range *L*_{s} with
*s* a chosen size property (e.g. diameter or volume). To transpose the variables from the continuous form (Eq. 1) to a discrete form,
*L*_{s} must be discretized in size bins *p* such that

where $\mathrm{\Delta}{s}_{p}={\int}_{{s}_{p}}^{{s}_{p+\mathrm{1}}}\mathrm{d}s$ is the size range of the bin *p*, and the terms $({s}_{p},{s}_{p+\mathrm{1}})$ indicate the lower and upper size bounds of
this bin. Therefore, the particle content of a given bin *p* can be interpreted as its mean value

Note that other particle properties (e.g. diameter, volume, density) are similarly interpreted as a bin-averaged value. For example, the
particle diameter corresponding to a given bin can be defined by ${D}_{p}=\frac{\mathrm{1}}{\mathrm{\Delta}{s}_{p}}{\int}_{{s}_{p}}^{{s}_{p+\mathrm{1}}}D\mathrm{d}s$. This diameter can
in turn be used to determine the particle volume using an allometric relationship such as ${V}_{p}={\mathit{\lambda}}_{\mathrm{1}}{D}_{p}^{{\mathit{\lambda}}_{\mathrm{2}}}$ (Jackson et al., 1997; Li et al., 1998; Zahnow et al., 2011)^{1}. Using this bin-averaged volume (*V*_{p}), the particle content can be
redefined as

where *λ*_{3} and *λ*_{4} are parameters empirically determined through field and laboratory experiments depending on the element chosen
(Alldredge, 1998; see Table 1).

The discrete form of Eq. (1) thus becomes

where *n*_{p} is the particle number concentration in *p* (i.e. the number of particles in *p*), and *N* the total number of resolved size
bin. In a closed system without sources and sinks of particles, the total concentration, ${C}_{\mathrm{T}}={\sum}_{p=\mathrm{1}}^{N}{C}_{p}$, is required to be
conserved over time. The time evolution of the concentration inside a given bin obeys the simple differential form

where *R*_{p} represents all reactions occurring in *p*.

## 2.2 Reaction for a triplet of particles

Coagulation and fragmentation are, by essence, reactions that involve three particles. Coagulation involves two particles, with indices *i* and *j*,
that collide and stick together to form a third, larger one with index *k*. Conversely, fragmentation can be considered as the opposite reaction where
a particle *k* breaks into two smaller ones *i* and *j*, in line with what was observed by Alldredge et al. (1990) in laboratory experiments. Reactions
involving more than three particles can always be decomposed as a sequence of triplet reactions. Note that *i* and *j* can originate from identical or
different size bins. In a linear size discretization, by definition, the *k* index always refers to a particle belonging to a different bin than
particles *i* and *j*. Then, the volume *V*_{k} of particle *k* resulting from the coagulation of particles *i* and *j*=1…*k* obeys

The reaction is arbitrarily built so that the *i*th particle is always the smallest one. From this assumption, the coagulation reaction can be
written as ${V}_{i}+{V}_{j}\Rightarrow {V}_{k}$, while fragmentation is ${V}_{k}\Rightarrow {V}_{i}+{V}_{j}$ (Fig. 1). However, in the case of a nonlinear
size discretization, this rule might be violated and this situation is discussed in Sect. 2.6.

In this model, the rules described above for particles will be applied on the bins' discrete spectrum of Eq. (5). For example, in a
coagulation reaction implying a given triplet of bins ($i,j,k$), the rate of change of the concentration in *i* will depend on that of *j*, and they
will conjointly prescribe the rate of change of *k*. Such a reaction for a triplet of bins can be interpreted as multiple reactions for triplets of
particles that can be found in their respective bins ($i,j,k$). For a reaction implying a given triplet of bins ($i,j,k$), the evolution of the
concentration in each bin is described by the following set of differential equations:

$\mathit{\delta}{C}_{i,j}^{k}$ is a triplet operator that represents both coagulation and fragmentation reactions acting on a given bin:

A bold index indicates the bin on which the reaction applies (see also Fig. 2 for a visual explanation of this convention). By
construction, of the total number of *k* particles involved in the reaction in Eq. (8), half of this number is associated with *i* and the
other half with *j* (Fig. 2), which explains why we multiply all the terms in Eq. (9) by $\mathrm{1}/\mathrm{2}$. 𝒦 is the
coagulation rate, while ℱ is the fragmentation rate. Coagulation has been studied extensively in previous works both in atmospheric
(Pruppacher and Klett, 2010) and oceanographic contexts (Jackson, 2001). It may be decomposed in a combination of a sticking probability and three main
collision mechanisms (Kiørboe et al., 1990; Ackleh, 1997; Engel, 2000; Jackson, 2001): Brownian motion, fluid velocity shear and differential
settling. Fragmentation (ℱ) can be driven by biology, e.g. related to zooplankton activities such as grazing (Banse, 1990; Green and Dagg, 1997),
and swimming behaviour (Dilling and Alldredge, 2000; Stemmann et al., 2000; Goldthwait et al., 2004), or driven by physics, e.g. scales of turbulence (Alldredge et al., 1990; Kobayashi et al., 1999).

## 2.3 Reaction matrices

Let us now consider a set of discrete bins (*p*) that are linearly incremented and have indices (*i*,*j*) running from 1 to *N*. By construction, this
yields reactions for *k* ranging from *k*=2 to 2*N* (i.e. 1+1 to *N*+*N*). To account for the concentration evolution associated with all possible
reactions, we define four matrices built from the triplet operator ($\mathit{\delta}{C}_{i,j}^{k}$) defined earlier, **D**_{i=j}, **B**_{i,j},
**T**_{j,i} and **F**_{i,k}, referring to diagonal, bottom, top and final matrices, respectively.

Matrix **D**_{i=j} has dimensions *N*×*N* and accounts for reactions in which the two particles have the same size (*i*=*j*)
(Fig. 2a and c):

The square brackets above indicate the matrix dimensions. Matrix **B**_{i,j} accounts instead for all reactions acting on *i* only (where
*i*<*j*, Fig. 2b and d):

while **T**_{j,i} accounts for those acting on *j* only (where *j*>*i*, Fig. 2b and d):

The fourth matrix contains all the reactions acting on *k*, which has dimensions *N*×2*N*, and is given by

In **F**, the double line separates the resolved size range (above) from the unresolved one (below). The latter contains reactions involving particle
sizes outside the resolved size range of the spectrum (i.e. reactions for which *k*>*N*).

### The unresolved range

Solution strategies to parameterize reactions in the unresolved range must obey two basic rules: (i) they must conserve the total concentration
(*C*_{T}) in absence of external sources and sinks of particles and (ii) they must limit the unbounded growth of the particle size due to
coagulation. Here we propose a simple closure to account for the reactions in this range. In order to comply with conservation of the total
concentration in the size range (*L*_{s}), at least one new bin must be added in which concentration fluxes in and out of the resolved range
are stored. Moreover, in order to avoid unbounded growth of the size range due to coagulation, this extra bin must not be allowed to further coagulate
with itself or any of the other particles sizes. As such, all reactions that fall into the unresolved range will be accounted for in a single
additional bin for which coagulation is prohibited but fragmentation back into the resolved range is allowed. This extra bin can thus be interpreted
as an average of all the reactions in the unresolved range ($N<k\le \mathrm{2}N$) and will be referred to as $k=\frac{\mathrm{3}}{\mathrm{2}}N$. Applying this to
Eqs. (10)–(13) yields matrices with dimensions $[N\times N+\mathrm{1}]$:

where Δ*C*=2*δ**C* when *i*=*j* and *δ**C* otherwise, and 𝒦=0 in Δ*C* for
$k=\frac{\mathrm{3}}{\mathrm{2}}N$. Equation (14) includes all reactions acting on either *i* or *j*, while Eq. (15) includes all
reactions acting on *k*. Since the unresolved range only involves reactions acting on *k*, all the terms below the double line in
Eq. (14) are set to zero. Moreover, each term of the last row in Eq. (15) can be viewed as a sum over all the
elements of a given column below the double line in Eq. (13). Note that, for simplicity, we choose to add only one extra bin in the
unresolved range, but one could alternatively choose to add up to *N* bins in order to improve this parametrization.

## 2.4 Summary of all reactions

Based on the matrices (Eqs. 14 and 15) and specific reaction rules previously described, the reaction
vector *R*_{p},
representing all the reactions for a given bin ($\mathrm{1}<p<N+\mathrm{1}$) is given by

where ** U** is a unit vector with dimension [

*N*],

**Coag** and **Frag**
in Eq. (16) are used to explicitly show the sign of the coagulation and fragmentation reactions from
Eq. (9). In other words, coagulation is removing concentration from *p* in *X*_{p}+*Y*_{p}, while adding concentration to *p* in *Z*_{p} (and
conversely for fragmentation). Equation (16) can be rewritten as a sum of the following series:

where Δ*C*=2*δ**C* when *p*=2*i* and *δ**C* otherwise. An example of a complete set of reactions with *N*=4 and its additional bin
$\frac{\mathrm{3}}{\mathrm{2}}N=\mathrm{6}$ is shown in Fig. 3.

Focusing on the resolved range only, Eqs. (16) and (18) can be combined to yield a discrete version of the generalized Smoluchowski equation (e.g. von Smoluchowski, 1916; Hansen, 2018):

where *δ*_{ip} is the Kronecker delta function that is equal to 1 for *i*=*p* and zero otherwise. Notice that the above equation gives the rate of
change of concentration, whereas the traditional formulation for the Smoluchowski equation is written in terms of the number of
particles. Equation (19) can thus be reformulated in terms of the number of particles as

The factor of $\mathrm{1}/\mathrm{2}$ in the second term ensures that the combination of two particles yields a single larger particle. This is in contrast to the concentration, for which the combination is additive (see Eq. 8).

## 2.5 Robustness to resolution

For a discrete representation of the full size range, *L*_{s}, larger values of *N* imply higher resolution of the size spectrum. The total
number of reactions of the system described above increases with the size of the reaction matrices, *N*(*N*+1), which is nearly a quadratic function of
resolution. On the other hand, coagulation and fragmentation are, respectively, quadratic and linear functions of concentration, as shown in
Eq. (9). Since concentration is itself a quantity that depends on resolution (Eq. 5), an asymmetric response between
coagulation and fragmentation to changes in resolution is expected.

In order to build an intuition on the effect of resolution on the reactions, consider a conservative system with a given total concentration,
${C}_{\mathrm{T}}={\sum}_{p=\mathrm{1}}^{N}{C}_{p}$, and a linear size discretization such that both the particle content and concentration are constants given by
𝒩_{p}=1 and ${C}_{p}={C}_{\mathrm{T}}/N$, respectively. To further simplify, assume that the rates of coagulation and fragmentation are constants
given by 𝒦 and ℱ, respectively. For a conservative system, the sum of all reactions integrates to zero,
i.e. ${\sum}_{p=\mathrm{1}}^{N+\mathrm{1}}{R}_{p}=\mathrm{0}$, such that *C*_{T} remains constant at all time. Since the sum of all reactions does not
allow to keep track of the total concentration exchanged along the spectrum, we instead define from Eq. (16) the total reaction amplitude as

and since $\mathcal{N}=\mathrm{1},$

where the first term on the right-hand side is the total coagulation amplitude and the second term is the total fragmentation amplitude. In this
simple example, dependence to resolution is revealed through the respective prefactors $\left(\frac{N+\mathrm{1}}{N}\right)$ and (*N*+1). It thus becomes
obvious that fragmentation is more sensitive to resolution than coagulation. This result can be explained a posteriori if we notice that the total
number of reactions is nearly quadratic with *N*, while the coagulation concentration is proportional to *N*^{−2}, cancelling most of the variation
with *N*. In contrast, the fragmentation concentration is proportional to *N*^{−1}, which yields a larger residual dependence on *N*. To
counterbalance this dependence on resolution, the reaction terms are divided by their respective resolution-dependent coefficients such that

While the general case with non-constant contents and concentrations yields a similar qualitative dependence on *N*, this simple parametrization
produces significant biases in the nonlinear experiment (E2) described in the Sect. 4.2. Thus, a more exhaustive study on robustness to resolution is
needed in order to improve this parametrization, and solutions strategies are discussed in Sect. 4.3.

## 2.6 Nonlinear size spectrum

For simplicity, we chose to describe the above framework using a linear size discretization (i.e. where bins are equally distributed along the size
range). However, this choice was arbitrary and we now generalize the framework to a nonlinear size discretization, which is a more natural choice for
representing marine particles. A nonlinear size discretization can be seen as local variations of the resolution in the full size range. For example,
for a given total number of bins, *N*, switching from a linear to a logarithmic spacing increases resolution for the small particles, while decreasing
it for the large particles. Intuitively, this choice seems better suited to represent marine particles that have a wider variety of microscopic than
macroscopic particles.

In this context, the main difference with the framework described in the previous sections is that volume conservation can be violated when using a
nonlinear size spectrum; i.e. Eq. (7) is no longer valid. Although counterintuitive, this does not imply however
that mass conservation is necessarily violated. Consider, for example, a simple nonlinear discretization where bins are each separated by an order of
magnitude (1, 10, 100 µm^{3}, etc.). Coagulation of particles belonging to bins 1 and 10 µm^{3} would ideally produce a particle
size of 11 µm^{3}. However, since 11 µm^{3} is much closer to bin 10 µm^{3} than bin 100 µm^{3} (the next
larger bin), all the concentration associated with this reaction will fall into the 10 µm^{3} bin, thus violating volume conservation, yet
conserving the concentration associated with the reaction. We thus modify Eq. (7) to allow that the resulting size
of a coagulation reaction is not required to be strictly equal to the sum of the two reacting particles (and conversely for fragmentation), i.e.

The main consequence of Eq. (23) is that it modifies the reaction matrices (Eqs. 14
and 15). In Eq. (14), only the *k* indices will be modified by a nonlinear discretization. In
Eq. (15), the elements themselves will be redistributed on different rows of the matrix. For a logarithmic discretization that
enhances resolution towards smaller particles, elements will be moved upwards in the **F** matrix as an increased number of reactions yield
${V}_{i}+{V}_{j}\phantom{\rule{0.25em}{0ex}}\ne \phantom{\rule{0.25em}{0ex}}{V}_{i+j}$. For example, $\mathit{\delta}{C}_{\mathrm{1},\mathrm{2}}^{\mathrm{3}}$ could be switched from the third row using a linear spectrum to $\mathit{\delta}{C}_{\mathrm{1},\mathrm{2}}^{\mathrm{2}}$ in the
second row using a logarithmic spectrum. Conversely, elements will be moved downwards in the **F** matrix if a discretization that enhances
resolution towards larger particles is chosen. Here, we do not explicitly write the matrix encompassing all possible cases since this would be
unnecessarily complex. The construction of the reaction matrices (Eqs. 14 and 15) is done numerically at the
beginning of our algorithm (see Gremion and Nadeau, 2021).

## 2.7 Application to a plankton ecosystem model

The framework presented here gives rise to *N* variables that together represent detritic particulate matter. Like other variables of plankton
ecosystem models, these additional variables must be treated as Eulerian tracers submitted to diffusive and advective transport. In a typical
three-dimensional OGCM, the evolution of the carbon concentration *C*_{p} (mmol C m^{−3}) belonging to the size bin *p* is given by

where $\mathit{u}=\mathit{\xee}u+\mathit{\u0135}v+\widehat{\mathit{k}}w$ is the velocity field and $\mathrm{\nabla}=\mathit{\xee}\frac{\partial}{\partial x}+\mathit{\u0135}\frac{\partial}{\partial y}+\widehat{\mathit{k}}\frac{\partial}{\partial z}$. The terms on the right side are (i) the
advective flux convergence, (ii) the diffusive flux divergence, with *K* the turbulent diffusivity, (iii) the background vertical advection due to the
settling velocity *w*_{p} associated to size bin *p*, (iv) the sources and (v) the losses of detritic matter associated with other biogeochemical
processes, and (vi) the reaction term, *R*_{p}, representing coagulation and fragmentation derived in this paper (Eq. 16). The
three-dimensional velocity *u* is provided by equations driving geophysical fluid dynamics. The vertical settling velocity, *w*_{p}, is here assumed
constant for a given size but can vary considerably from one size to another as it strongly depends on particle properties such as its volume,
density and porosity. Therefore, *C*_{p} does not vary due to differential settling (divergence or convergence), but *C*_{T} can through the
action of the reaction term, *R*_{p}. In order to focus uniquely on the reaction term, *R*_{p}, we do not solve
Eq. (24) explicitly in this work and leave this for a subsequent study. In the following, we use the simplified
zero-dimensional form $\partial {C}_{p}/\partial t={R}_{p}$ to investigate the robustness of the proposed framework on the resolution, *N*.

Two model configurations are set up to study detritic carbon concentration (*C*) dynamics experiencing coagulation and fragmentation reactions using
the previously described model. The first configuration, named E1, uses a linear size discretization over the arbitrarily chosen range of 0 to 8
(Fig. 4a). The second configuration, identified as E2, uses a nonlinear size discretization that more realistically represents particle
number distributions observed in the ocean, i.e. particle size from *D*_{1} = 1 µm to *D*_{N} = 1 cm
(Stemmann et al., 2004; Monroy et al., 2017) (Fig. 4b). Each set of experiments within the configurations is performed using two different numbers of
size bins (*N*) in order to study the impact of resolution on the model. The high-resolution (HR) simulation uses *N*=400 size bins,
while the low-resolution (LR) simulation uses *N*=4. An additional bin having an index value of $\frac{\mathrm{3}}{\mathrm{2}}N$ is added to represent
the unresolved size range, which increases
the total number of bins to *N*=401 and *N*=5, respectively. For each resolution, three simulations are performed: two simulations where coagulation
(K) and fragmentation (F) are considered separately, and one simulation where they are combined (KF). In total, 12 simulations are performed with
parameter values summarized in Tables 1 and 2.

## 3.1 Initialization

For each configurations, all experiments are initialized from a reference distribution at very high resolution (i.e. ${N}^{\u2020}=\mathrm{4000}$ bins) that is
meant to represent an ideal distribution. This discretization is indicated by the symbol †. The total carbon concentration is set to
*C*_{T} = 100 mmol C m^{−3}, and initial concentrations, *C*_{p}(*t*=0), are then initialized for HR and LR by integrating on the
reference distribution using a discrete version of Eq. (3):

where *p* refers to indices of the HR and LR discretizations and *q* to indices of the reference distribution. For HR and LR in configuration E1, the
initial carbon concentration of the reference is uniform (i.e. ${C}_{p}^{\u2020}=\frac{{C}_{\mathrm{T}}}{{N}^{\u2020}}$) as for the particle carbon content
(𝒩_{p}=1). The chosen time step is Δ*t* = 86 400 s, and it is run for one time step.

In E2, the reference is initialized with a power-law-distributed carbon concentration of the form (McCave, 1984; Li et al., 2004)

where *n* is the number of particles of diameter *D* and Ψ is the slope of the distribution, by connecting with Eq. (5). Unlike in E1,
particle carbon content is set to a size-dependent function following Eq. (4). HR and LR initial carbon concentration distributions are
then obtained following Eq. (25), and the model is integrated for a day with a time step Δ*t* = 3600 s.

In both configurations (E1 and E2), in order to compare results from both simulations and assess the resolution dependence of the model, HR carbon concentrations are mapped to the LR discretization following Eq. (25). Moreover, in order to simplify the problem and to focus only on the resolution dependence of the framework, coagulation and fragmentation rates, 𝒦 and ℱ, respectively, are set to constant values (Table 2).

## 4.1 Linear discretization

Figure 5 shows the results from the linear size discretization (E1), for both LR and HR simulations and for coagulation and fragmentation considered separately as well as simultaneously.

Starting with initial uniform carbon concentration distribution, coagulation leads to a reduction of *C*_{p} in small size bins and an increase in
larger ones for both LR and HR, resulting in a linearly increasing distribution of *C*_{p} over the resolved size range (*p*=1 to *N*,
Fig. 5a, b). The largest accumulation of *C*_{p} appears in the unresolved size range ($p=\frac{\mathrm{3}}{\mathrm{2}}N$) for both LR and HR
simulations. Notice that the carbon concentration in the unresolved bin of the HR experiment is divided by a factor of 100 in order to fit in the *y*
scale. However, a smaller amount of particles end up in this range in the HR simulation compared to the LR (Fig. 5c), which is associated
with larger final concentrations *C*_{p} in the resolved range for HR since the model is conservative by design. The final carbon concentration
distributions are nonetheless very similar considering the very large difference in the number of bins between LR and HR.

In contrast with coagulation, fragmentation yields a reduction of *C*_{p} in larger size bins to the benefit of an increase in the small ones
(Fig. 5d–f). Only very small differences of *C*_{p} are noticeable between LR and HR over the resolved range (Fig. 5f),
suggesting that fragmentation is very weakly dependent on the size range resolution when a linear discretization is used. As the unresolved range is
initialized to zero, and fragmentation does not allow particle to increase in size, ${C}_{\frac{\mathrm{3}}{\mathrm{2}}N}$ remains zero for both simulations.

When both reactions are combined and act simultaneously, they nearly compensate each other in small size bins in both simulations
(Fig. 5g and h) but to a greater extent in the HR case. For larger size bins however, fragmentation seems to operate nonlinearly and more
strongly than coagulation, leading to a smaller carbon concentrations related to large particles compared to the initial value. The comparison
(Fig. 5i) shows a significantly greater *C*_{p} in each resolved size bins for HR compared to LR, but it is the inverse for the unresolved
range. This is explained by the asymmetry that exists in the mathematical formulation of coagulation and fragmentation (Eq. 9), with the
former being a quadratic function of concentration, while the latter is a linear function. Overall, despite the fact that coagulation and fragmentation
do not compensate for each other, which is not a prerequisite in the model, the dependence on the number bins for a given range of particle sizes is quite
weak when using a linear discretization.

These results demonstrate that in a linearly size discretized configuration, the model is reasonably independent of the resolution when coagulation and fragmentation are used independently or combined (Fig. 5). They show however the importance of considering the unresolved size range in the model design, which guarantees mass conservation and unbounded growth due to coagulation.

## 4.2 Nonlinear discretization

We now consider the results from the nonlinear size discretization model (E2), shown in Figs. 6 and 7. Recall that instead of
a uniform *C*_{p} distribution, the model is initialized with a distribution that is exponentially decreasing over a size range from 10^{−6} m to
10^{−2} m, thus spanning 4 orders of magnitude (Eq. 26, Table 2). In this case, results differ as a function of
resolution for the reactions taken separately or combined. All LR simulations (K, F and KF) react more strongly than the HR ones.

Focusing first on the resolved range in the coagulation-only experiment, we observe a diminution of *C*_{p} in the smaller size bins and an accumulation
in the larger size bins in LR simulation (Fig. 6a). However in HR, this distribution pattern is limited to a small range of sizes between
10^{−6} m and 10^{−4} m (Fig. 6b). In addition, no significant accumulation of concentration is observed in the largest size bins, and
by extension neither into the unresolved range ($p=\frac{\mathrm{3}}{\mathrm{2}}N$). This leads to a difference of *C*_{p} regarding the larger size bins between LR and
HR when mapped on the same grid (Fig. 6c), with the LR overestimating carbon concentration for larger size bins. Figure 7
shows the time evolution of the distributions of Fig. 6. In the LR case, the initial response (roughly 2 h) is characterized by a fast
timescale, followed by a slower response during the rest of the simulation (Fig. 7a). In contrast, in the HR case, the response is
localized in the small size range and only the slower response is observed (Fig. 7b). While attenuated, these biases are still clearly
visible when HR is remapped on LR (Fig. 7c).

Results for fragmentation only yield a similar biases between the LR and HR cases. Reactions are magnified in LR compared to HR (panels d–f of Figs. 6 and 7). When both reactions are combined, coagulation dominates over fragmentation to explain most of the observed changes in distribution of the LR simulation (Fig. 6g). In HR, both coagulation and fragmentation have localized effects on the smallest and largest size ranges (Fig. 6h). The comparison of HR vs. LR shows a pattern that is similar to the comparison between resolutions for the coagulation reaction (Figs. 6i and 7i). This indicates that coagulation dominates over fragmentation, which is expected for an initial concentration distribution that is highly skewed towards small particles.

These simulations demonstrate that when using a nonlinear size discretization and a nonlinear initial carbon concentration distribution, the model
behaviour is significantly dependent on resolution. To attenuate this dependence, which arises from the asymmetry between coagulation and
fragmentation, we propose adding and tuning a penalty function that will compensate this difference as *N* varies.

## 4.3 Resolution dependency function

The residual dependence of the model to resolution in the nonlinear discretization arises mainly from the fact that the prefactors used in
Eq. (22) are derived from a linear analysis, which yields an asymmetry between coagulation and fragmentation. To minimize the
effect of this asymmetry, we propose multiplying both reaction terms by a resolution-dependent function *f*(*N*) that is positive and monotonically
varies between a value to be determined at low *N* and 1 for *N*→∞. For simplicity, we here assume an exponential function of the
form

with *λ*_{5} and *λ*_{6} positive constant parameters that were determined empirically (see Table 1).

Simulation results obtained with this correction factor (Figs. 8 and 9) show that both LR and HR simulations now agree much better. The LR simulation is the one that is the most impacted by this change (Fig. 8a–c), as $f(N=\mathrm{400})=\mathrm{0.9878}$ and $f(N=\mathrm{4})=\mathrm{0.0526}$. Comparing Fig. 6c, f and i with Fig. 8c, f and i, it is clear that a carefully chosen penalty function such as that in Eq. (27) can significantly reduce the error attributed to a number of size bins as low as four. The fundamental cause of the resolution dependency seems to be linked with the nonlinear size discretization, but it is not clear how the penalty function can be determined in a simple way from prior knowledge. Is this solely dependent on the choice of discretization, or is it also dependent on how particles are distributed along that spectrum? This remains an open question.

We have developed a new 0-D numerical model for representing coagulation and fragmentation as an interaction between three particles of arbitrary sizes. Particles are categorized in size bins that can be linearly or nonlinearly distributed along a given size spectrum. In the linear configuration, E1, the total volume of suspended particulate matter (i.e. the sum of the volume of all individual particles) is also conserved. However, this is not strictly the case in the nonlinear configuration, E2, because it can happen that two particles of two different size bins can end up in the same size bin as the biggest one. By construction, the total concentration of carbon carried by particles is conserved over the resolved and unresolved range. The unique arbitrary size bin $\frac{\mathrm{3}}{\mathrm{2}}N$ offers (i) boundaries to avoid any exponential growth of the size range and (ii) reduces falsified carbon concentration estimation in the larger size bins. When absent, accumulation of particles was observed (exploratory studies of our current work which are not shown). This caveat is present in models using the sectional approach, the solution of which is to increase the number of bins, as described by Burd (2013).

Coagulation has a quadratic dependence on particle number concentration participating to the reaction, while fragmentation has a linear dependence on
the particle number concentration. The linear configuration has a very weak dependence on the size spectral resolution (number of size bins *N* for a
given size range). The nonlinear configuration has a significant dependence to resolution. This dependence can be overcome by multiplying both
reaction terms with a function *f*(*N*) such that *f*(*N*)→1 when *N*→∞. However, further work is required to unearth what is
causing the dependence, as the assumption made that reactions need to be divided by the inverse dependent resolution coefficient
(Eq. 22) for both linear and nonlinear cases was partly wrong. The surmise that *C*_{p} will be equally distributed between the
size bins (i.e. ${C}_{p}=\frac{{C}_{\mathrm{T}}}{N}$) is not respected in the nonlinear case as distribution assigned is nonlinear over the size bins
(Eq. (26), i.e. ${C}_{p}={C}_{\mathrm{T}}\frac{{\mathrm{\Delta}}_{p}}{\sum {\mathrm{\Delta}}_{p}}$) in addition to be coupled to a nonlinear size discretization. The
presented attempt to rectify this wrong assumption via the elaboration of the function (Eq. 27) for the nonlinear case allows prospects
to thrive and reassure that solutions exist. Despite this, the model succeeds in representing the evolution of the size of suspended particulate
matter due to the simultaneous action of coagulation and fragmentation using a low number of size bins (*N*=4). This is comparable to biogeochemical
models of low (e.g. Fasham et al., 1990, with seven state variables, or Neumann, 2000, with nine) to moderate complexity
(e.g. Aumont et al., 2015,
with 24 prognostic variables, or Ward et al., 2012, with more than 50). However, the sensitivity of our model outcomes to many arbitrary constant parameters
needs to be profoundly investigated, as some of them may be size dependent and therefore vary along the size range. Such parameters are the
coagulation rate and associated stickiness of particles (𝒦, Eq. 9) and the fragmentation rate
(ℱ, Eq. 9). Other parameter values, such as the slope of the particle distribution (Ψ, Eq. 26) and
parameters relying on the carbon content estimation of each particles (𝒩, Eq. 4), were chosen according to literature,
determined through field or laboratory studies for specific geographic regions or ecosystem states. The total initial carbon concentration over the
size range (*C*_{T}) will also require deeper investigation, as by acting jointly with other parameters it may affect reaction thresholds
in the model, and the final outcomes and conclusions. Lastly, the role of coagulation and fragmentation reactions on the cell density behaviour along
the size range will be required (Gregory, 1997) in the parameterization of the settling velocity for each detritic state variable
implemented. This step will be required to incorporate the model in a 1-D environment coupled to physical fields. Ultimately, when reliably
parameterized, this model will be coupled to an upper-trophic-level ecological model and OGCMs that will enable addressing further questions related
to the fate of particle evolution with depth.

Through our approach, a balance between a low computational cost and a proximity to particulate organic matter ecological dynamics expected to be found in the ocean was fulfilled. This is a first attempt to fill the knowledge gap underlined by Boyd et al. (2019) by offering a model of particle transformations to incorporate in OGCMs. This work, as well as the steps to come, will then offer new perspectives to estimate the downward carbon export in global models, as coagulation and fragmentation reactions will be characterized alongside of other known processes affecting the vertical flux of organic matter (e.g. grazing, remineralization). Thus, comparisons with previous studies will be possible to conclude on the influence to consider or not these often-ignored reactions on the estimation of the biological pump's response to climate change.

The current version of the model and user manual are freely available and can be downloaded from the referenced project page: https://doi.org/10.5281/zenodo.4432896 (see Gremion and Nadeau, 2021). The code concedes to the GNU General Public License v2.0 and is written in Fortran 90 and requires the gfortran compiler. MATLAB 2015b minimum is required to reproduce the figures using the scripts provided in the Zenodo archive.

The data, analysis and figures presented in this paper can be generated using the model files available in the “Code availability” section and user-defined parameters.

GG and LPN co-built the numerical framework and prepared the figures; GG ran the analysis and wrote the text; GG, LPN, CD and DD participated in discussions prior to model setup; LPN, CD, IRS, PA and DD provided edits and reviews during manuscript preparation. DD secured funding to support the development of the model.

The authors declare that they have no conflict of interest.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The work was conducted within the framework of ArcticNet, a Network of Centres of Excellence of Canada, and of the Quebec Ocean strategic network.

This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant to Dany Dumont (grant no. 402257-2013) and Arc3Bio (Marine Biodiversity, Biological Productivity and Biogeochemistry in the Changing Canadian Arctic), an ArcticNet project.

This paper was edited by Sylwester Arabas and reviewed by two anonymous referees.

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^{1}

The two constants parameters *λ*_{1} and *λ*_{2} depend on particles and can be obtain from laboratory
experiments (Jackson et al., 1997) or set by the user (Table 1).