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Ocean biogeochemistry in the Canadian Earth System Model

Abstract. The ocean biogeochemistry components of two new versions
of the Canadian Earth System Model (CanESM) are presented and compared to
observations and other models. CanESM5 employs the same ocean biology model
as CanESM2, whereas CanESM5-CanOE (Canadian Ocean Ecosystem model) is a
new, more complex model developed for CMIP6, with multiple food chains,
flexible phytoplankton elemental ratios, and a prognostic iron cycle. This
new model is described in detail and the outputs (distributions of major
tracers such as oxygen, dissolved inorganic carbon, and alkalinity, the iron
and nitrogen cycles, plankton biomass, and historical trends in CO2
uptake and export production) compared to CanESM5 and CanESM2, as well as to
observations and other CMIP6 models. Both CanESM5 models show gains in skill
relative to CanESM2, which are attributed primarily to improvements in ocean
circulation. CanESM5-CanOE shows improved skill relative to CanESM5 for most
major tracers at most depths. CanESM5-CanOE includes a prognostic iron
cycle, and maintains high-nutrient/low-chlorophyll conditions in the
expected regions (in CanESM2 and CanESM5, iron limitation is specified as a
temporally static “mask”). Surface nitrate concentrations are biased low in
the subarctic Pacific and equatorial Pacific, and high in the Southern
Ocean, in both CanESM5 and CanESM5-CanOE. Export production in CanESM5-CanOE
is among the lowest for CMIP6 models; in CanESM5, it is among the highest,
but shows the most rapid decline after about 1980. CanESM5-CanOE shows some
ability to simulate aspects of plankton community structure that a
single-species model can not (e.g. seasonal dominance of large cells) but
is biased towards low concentrations of zooplankton and detritus relative to
phytoplankton. Cumulative ocean uptake of anthropogenic carbon dioxide
through 2014 is lower in both CanESM5-CanOE (122 PgC) and CanESM5 (132 PgC)
than in observation-based estimates (145 PgC) or the model ensemble mean
(144 PgC).



Supplementary
. In Figure 10, Western Subarctic Pacific and Eastern Subarctic Pacific are combined as "North Pacific" (latitude boundaries as below). A map of the regions is shown in Figure S5.

Aeolian iron dissolution
Aeolian iron was introduced assuming a fractional solubility of 1.4% in the surface layer and adopting the PISCES parameterization for subsurface dissolution. According to Aumont et al (2015) this assumes a sinking speed of 2 m/d and a dissolution rate of 0.0001 d -1 , which translates to an e-folding length scale of 20000 m and is consistent with the NEMO 3.4 PISCES code, except for the length scale, which is set to a much smaller 1000 m.
We can think of this parameterization as the divergence dF/dz of a flux that declines exponentially with depth: where k is a first-order dissolution rate and w is the sinking speed (e.g., Christian et al., 1997).
Because the subsurface layers are much thicker than the surface layer, the total amount of iron introduced results in an effective fractional solubility of aeolian iron that is much larger than the specified 1.4%. The whole water column dissolution is 6.4%, of which 22% is introduced in the surface layer. Note that if the somewhat arbitrary substitution of an e-folding length scale of 1000 m for 20000 m were not done, the difference would be much larger: a net fractional solubility of 25%, of which only 5.5% would go into the surface layer. (All of these numbers are grid-specific and are for the ORCA1 levels.) Because in our model the rate of scavenging loss is very high for concentrations above 0.6 nM, much of this additional aeolian iron will be removed without ever entering the euphotic zone. While the net dissolution of the total aeolian iron input is much higher than the specified fractional solubility of 1.4%, it is important to note that in terms of biological response it is clearly not equivalent to a model that supplies aeolian Fe only to the surface layer with a fractional solubility of 6.4%. It is probably closer to a model with 1.4% and no subsurface dissolution, although exactly where it falls on this spectrum will vary with region and season.

Dissolved iron model comparison to observations
As there is no gridded global data product for dissolved iron (dFe) we present here some comparisons with individual bottle samples from the GEOTRACES Intermediate Data Product 2017 (Schlitzer et al., 2018), the MBARI data compilation (www3.mbari.org/chemsensor/Data/), and the Pacific data set compiled by PICES WG22 (meetings.pices.int/members/working-groups/disbanded/wg22). The MBARI data include both profile (the "Global Iron Data") and surface transect ("MBARI SOLAS") data (the SOLAS data also include some profiles but are primarily underway surface measurements). The GEOTRACES data are primarily profile data (393 profiles total, only ~10% of 7519 data are <50 m). Most of the MBARI data are also in the PICES data set; these were preprocessed to remove redundant data. Data from the underway surface transects were excluded to avoid overweighting these regions due to autocorrelation, leaving a total of 3575 data points. Concentrations > 2.5 nM, which comprise 1.3% of GEOTRACES data and 3.3% of PICES/MBARI data, were excluded from the model/data comparisons below, except for the individual GA-02 profiles.
Evaluating the CanOE model beyond what has been discussed in the main text identified several key points, most of which have already been made to some degree. CanESM5-CanOE compares favourably with other CMIP6 models but is quite biased towards a 'nutrient-type' rather than 'scavenged-type' profile (Figures S10b,c,. In CanOE rates of Fe scavenging are very high above 0.6 nM dFe and very low below this concentration, resulting in an almost constant deep water concentration of 0.6 nM. The collected data (of which there are >10000 vs only a few hundred in 1997) show that the basic hypothesis articulated by Johnson et al. (1997) still holds: deep-water concentrations are generally close to 0.6 nM, although much higher concentrations are sometimes observed at depths of thousands of metres ( Figure S10b, see e.g. Resing et al., 2014). High concentrations are also sometimes observed in near-surface waters ( Figure S10b) and CanESM5-CanOE is among the models least able to reproduce these. CanESM5-CanOE consistently overestimates the lowest observed concentrations and underestimates the higher ones ( Figure S10d). One might argue that other models do little better in a statistical sense. However, in several models the deviations from the 1:1 line are less systematic than in CanESM5-CanOE; the residuals may be larger but they are more homogeneous. This comparison also largely confirms the results of Séférian et al. (2020, their Figure 5), although only two of the four models shown here were included in their analysis. CNRM-ESM2-1 shows a capacity to simulate the full range of dFe concentrations, as was the case in Séférian et al., although in no case is the spatial pattern correlation very large (in Séférian et al., CNRM-ESM2-1 was the second highest at 0.21). Nonetheless, this analysis indicates that purely statistical comparisons as in Figures S10e-g can be misleading. CanESM5-CanOE shows fairly good skill by these metrics but has systematic biases. These biases can clearly be attributed to the rather simplistic scavenging model employed, and will be addressed in future versions.
We made direct comparisons of our modelled dissolved iron concentrations with GEOTRACES transect data for GA-02 in the Atlantic (e.g., Middag et al. 2015), which was the most spatially extensive transect available ( Figure S10a). We show depth profiles from 47°S to 47°N. Mostly this confirms what we already knew from the other analyses presented: our model has a very low scavenging rate below 0.6 nM and a very high rate above, so that deep water concentrations are quite uniform and near-surface concentrations are biased low in high-deposition regions like the northern tropical Atlantic. For the most part, the model reproduces the observed concentrations quite well, given these known biases. What we learn from including this additional analysis is (a) the model is biased high in the Antarctic Bottom Water (Middag et al. 2015 give an excellent presentation of the location and biogeochemical properties of this water mass), and (b) the seasonal biological drawdown in the mid-latitude North Atlantic is weak. The former is probably due mainly to the low scavenging rate at concentrations <0.6 nM, although it may also indicate a high bias in surface waters of the source region (see section 3.3). The latter is probably related to the generally low rate of export production ( Figure  19) and the weak North Atlantic spring/summer bloom (Figures 16 and 17). Model annual mean data are shown, but seasonal minima are not much less, compared to the strong summer drawdown in the observations. Figure S10a -Global distribution of dissolved iron (dFe) measurements in the GEOTRACES (black/green) and PICES/MBARI (blue) data compilations. GEOTRACES Pacific data north of 25ºS are indicated in green. Blue dots outside the Pacific are MBARI data. Large symbols indicate stations along GA-02 that are compared in Figure S10h. Figure S10b -All observations of dissolved iron (dFe) concentration (<2 nM) plotted against sampling depth. GEOTRACES data are in black and PICES/MBARI data are blue. Model data are global mean profiles of annual mean data as in Figure 13 in the main text. Figure S10c -Mean of observations of dissolved iron (dFe) concentration (<2 nM) for various depth strata, including both GEOTRACES and PICES/MBARI data. Means of all data available in that depth range; spatial coverage is very incomplete and inconsistent among the depth strata. Averaging layers are 50 m (0-1000 m), 100 m (1000-3000 m) or 300 m (>3000 m) thick. Model data are global mean profiles of annual mean data as in Figure 13 in the main text. Figure S10d -Modelled and observed mean dissolved iron (dFe) concentrations (excluding those >2.5 nM) in the upper 50 m at locations where a depth profile was taken (N=1122). Observed data are means of all measurements made at depths <50 m within a given profile. Model data are climatological surface values for the month in which the observed data were collected (red: CanESM5-CanOE; black CNRM-ESM2-1; blue: GFDL-CM4; magenta: MPI-ESM1-2-LR). Selection of models is somewhat arbitrary but includes the models that show the highest overall skill according to the metrics shown in Figures S10e-g. Thin lines are linear regressions for individual models; thick black line is 1:1. Figure S10e -Root mean square error and correlation coefficient for surface dissolved iron data (upper 50 m mean with maximum of 2.5 nM, as in Figure S10d) for CMIP6 models for which seasonal data were available. Figure S10f -As S10e but for the Pacific only (north of 25ºS). Figure S10g -As S10e but excluding the Pacific north of 25ºS. Figure S10h -Depth profiles for modelled and observed dissolved iron along the GA-02 transect of the Atlantic Ocean. Station locations are shown in Figure S10a (every third station, sorted by latitude). Model data are annual means.