Global Production Centre GLOBAL REANALYSIS BIO 001 029 · QUID for Global Ocean Biogeochemistry Hindcast GLOBAL_REANALYSIS_BIO_001_029 Ref: Date: Issue: CMEMS-GLO-QUID-001-029 03

Global Production Centre GLOBAL_REANALYSIS_BIO_001_029

Issue: 1.1

Contributors: Coralie Perruche, Camille Szczypta, Julien Paul, Marie Drévillon

Approval date by the CMEMS product quality coordination team: 02/07/2019

QUID for Global Ocean Biogeochemistry Hindcast

GLOBAL_REANALYSIS_BIO_001_029

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CHANGE RECORD

Issue Date § Description of Change Author Validated By

1.0 September 2018

All Creation of the document Coralie Perruche

Marie Drévillon

1.1

Avril 2019 IV.9 (new)

IV.10 (new)

IV.11 (updated)

Extension in 2018 + addition of 2 new variables: ph and spco2

Coralie Perruche

Marie Drévillon



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TABLE OF CONTENTS

I Executive summary ..................................................................................................................................... 4

I.1 Products covered by this document ........................................................................................................... 4

I.2 Summary of the results .............................................................................................................................. 4

I.3 Estimated Accuracy Numbers ..................................................................................................................... 5

I.3.1 Chlorophyll in Log10 ............................................................................................................................. 5

II Production system description .................................................................................................................... 7

II.1 PISCES Biogeochemical model description ................................................................................................ 7

II.2 Ocean physics and sea ice forcings: FREEGLORYS2V4 ................................................................................ 8

II.3 Atmospheric forcings: ERA-Interim ........................................................................................................... 9

III Validation framework ............................................................................................................................... 10

IV Validation results ...................................................................................................................................... 12

IV.1 Chlorophyll ............................................................................................................................................ 12

IV.1.1 Climatological biases ...................................................................................................................... 12

IV.1.2 Seasonal cycle and its interannual variability ................................................................................ 13

IV.1.3 Vertical distribution ....................................................................................................................... 18

IV.2 Nitrate ................................................................................................................................................... 19

IV.3 Phosphate .............................................................................................................................................. 22

IV.4 Silicate ................................................................................................................................................... 23

IV.5 Dissolved oxygen ................................................................................................................................... 24

IV.6 Phytoplankton concentration in carbon ................................................................................................. 26

IV.7 Iron ........................................................................................................................................................ 26

IV.8 Primary production ................................................................................................................................ 28

IV.9 Surface partial pressure of carbon dioxide ............................................................................................. 28

IV.10 pH ........................................................................................................................................................ 29

IV.11 Global balances .................................................................................................................................... 30

V System’s Noticeable events, outages or changes ...................................................................................... 31

VI Quality changes since previous version ..................................................................................................... 32

VII References ............................................................................................................................................ 34



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I EXECUTIVE SUMMARY

I.1 Products covered by this document

The product described in this document is the global biogeochemistry hindcast also referred to as GLOBAL_REANALYSIS_BIO_001_029. It is a multi year product that spans from year 1993 to year 2018.

It consists of daily and monthly mean fields of several biogeochemical variables (Nitrate, Phosphate, Silicate, Iron, Dissolved Oxygen, Chlorophyll, Phytoplankton Biomass, Primary Production, pH, surface partial pressure of carbon dioxide) at ¼° horizontal resolution over the global ocean.

I.2 Summary of the results

The quality of the global biogeochemical system has been assessed over the whole simulation period (1993 – 2018). The headline results for each of the variables assessed are as follows:

Chlorophyll: At sea surface, modelled chlorophyll fields show a good agreement with satellite data. The large-scale structures corresponding to specific biogeographic regions (double-gyres, Antarctic Circumpolar Current, etc.) are well reproduced. However the arrow shape tongue of chlorophyll in the Tropical Pacific is slightly too wide and expands too far westward. Concerning the temporal variability, our model succeeds well in reproducing the seasonal cycle at mid- and high- latitudes (spring bloom), but the timing of the bloom is not in phase with observations (one or two-month lag). On the global ocean, the modelled chlorophyll has an RMS difference of 0.371 for period 1997-2017 at sea surface in comparison with daily satellite chlorophyll observations.

Nutrients (NO3, PO4, Si): They display a good agreement with World Ocean Atlas Climatology at global scale except in the Southern Ocean where the concentration of nutrients are too high. South of 60°S, it is preferable not to use this product.

Dissolved Oxygen: At sea surface oxygen is mainly controlled by temperature. In subsurface and deep layers, the model is able to reproduce OMZs (oxygen minimum zone). South of 60°S, it is preferable not to use this product.

Surface partial pressure of CO2: Good agreement at large scale. Too high values in Eastern Boundaries upwellings

pH: Relevant patterns at large scale, but the pH presents a global bias of 0.02 (the model is too acid)

Important notice: Biogeochemical cycles are very long to balance. This simulation is not long enough for each variable to be balanced. A drift still remains. In particular, nutrients and dissolved oxygen are almost balanced in the surface layers but not below the surface layer. Phytoplankton variables (chlorophyll, phytoplankton carbon concentration, primary production) which are restricted to the euphotic layer reach an asymptotic behaviour after 2000.



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I.3 Estimated Accuracy Numbers

I.3.1 Chlorophyll in Log10

The Estimated Accuracy Numbers for chlorophyll in log10 are given in Erreur ! Source du renvoi introuvable. for years 1997 – 2017, which is the period covered by remote sensing ocean colour. They are computed on daily fields. The model is compared with the OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_085 product (dataset-oc-glo-chl-multi-l3-gsm_25km_daily-rep-v02). It is a multi-sensors gridded product (L3). The daily model fields are co-located on observations, thus the mean is done on the same number of samples.

Region MEAN STD RMSD BIAS

GLO OBS -0.688 OBS 0.446 0.371 -0.071

MOD -0.759 MOD 0.346

ARC OBS -0.096 OBS 0.447 0.624 -0.235

MOD -0.332 MOD 0.459

BALTIC OBS 0.321 OBS 0.355 0.970 -0.845

MOD -0.524 MOD 0.351

NORDSEA OBS 0.029 OBS 0.416 0.573 -0.316

MOD -0.287 MOD 0.375

IBI OBS -0.200 OBS 0.395 0.551 0.669

MOD -0.469 MOD 0.392

NAT OBS -0.592 OBS 0.501 0.421 -0.127

MOD -0.719 MOD 0.427

TAT OBS -0.806 OBS 0.371 0.333 -0.120

MOD -0.926 MOD 0.266

Table 1: Root Mean Square Difference √< (𝐨𝐛𝐬 −𝐦𝐨𝐝)𝟐 > and Mean Difference/ Bias < (mod-obs) >calculated on daily fields at sea surface for period 1997-2017 (units: log10 (mg.m-3)) (continues in next page).



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Region MEAN STD RMSD BIAS

SAT OBS -0.768 OBS 0.418 0.324 -0.057

MOD -0.825 MOD 0.345

NPA OBS -0.695 OBS 0.443 0.339 -0.078

MOD -0.773 MOD 0.351

TPA OBS -0.786 OBS 0.366 0.271 -0.059

MOD -0.845 MOD 0.288

SPA OBS -0.741 OBS 0.387 0.314 -0.019

MOD -0.760 MOD 0.350

IND OBS -0.732 OBS 0.370 0.346 -0.182

MOD -0.914 MOD 0.260

Table 2: Root Mean Square Difference √< (𝐨𝐛𝐬 −𝐦𝐨𝐝)𝟐 > and Mean Difference/ Bias < (mod-obs) >calculated on daily fields at sea surface for period 1997-2017 (units: log10 (mg.m-3))

Figure 1: Maps of the different sub-regions used to compute the MEAN, STD, RMSD, BIAS statistics



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II PRODUCTION SYSTEM DESCRIPTION

The product GLOBAL_REANALYSIS_BIO_001_029 described in this document has been produced at Mercator-Ocean (Toulouse, France).

The product GLOBAL_REANALYSIS_BIO_001_029 is a global biogeochemical simulation using the PISCES model (available on the NEMO1 platform). There is no data assimilation in this product. It is forced by daily mean fields of ocean, sea ice and atmosphere. Ocean and sea ice forcings come from the numerical simulation FREEGLORYS2V42 produced at Mercator-Ocean and the atmospheric forcings come from the ERA-Interim3 reanalysis produced at ECMWF.

- Production centre name: Mercator-Ocean, Toulouse, France

- Production system name: Global Ocean Biogeochemistry Hindcast (reference in the catalogue: GLOBAL_REANALYSIS_BIO_001_029)

II.1 PISCES Biogeochemical model description

PISCES short description

The biogeochemical model used is PISCES (Aumont, 2015). It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of NEMO modeling platform. It has 24 prognostic variables and simulates biogeochemical cycles of oxygen, carbon and the main nutrients controlling phytoplankton growth (nitrate, ammonium, phosphate, silicic acid and iron). The model distinguishes four plankton functional types based on size: two phytoplankton groups (small = nanophytoplankton and large = diatoms) and two zooplankton groups (small = microzooplankton and large = mesozooplankton). Prognostic variables of phytoplankton are total biomass in C, Fe, Si (for diatoms) and chlorophyll and hence the Fe/C, Si/C, Chl/C ratios are variable. For zooplankton, all these ratios are constant and total biomass in C is the only prognostic variable. The bacterial pool is not modeled explicitly. PISCES distinguishes three non-living pools for organic carbon: small particulate organic carbon, big particulate organic carbon and semi-labile dissolved organic carbon. While the C/N/P composition of dissolved and particulate matter is tied to Redfield stoichiometry, the iron, silicon and carbonate contents of the particles are computed prognostically. Next to the three organic detrital pools, carbonate and biogenic siliceous particles are modeled. Besides, the model is able to resolve the carbonate system with the variables dissolved inorganic carbon and total alkalinity. In PISCES, phosphate and nitrate + ammonium are linked by constant Redfield ratio (C/N/P = 122/16/1), but cycles of phosphorus and nitrogen are decoupled by nitrogen fixation, denitrification and external sources.

The distinction of two phytoplankton size classes, along with the description of multiple nutrient co-limitations allows the model to represent ocean productivity and biogeochemical cycles across major biogeographic ocean provinces (Longhurst, 1998). PISCES has been successfully used in a variety of

1 https://www.nemo-ocean.eu/modelling

2 https://www.mercator-ocean.fr/en/solutions-expertise/how-to-access-the-mercator-ocean-services/let-s-define-your-needs/

3 https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim



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biogeochemical studies (e.g. Bopp et al. 2005; Gehlen et al. 2006; 2007; Schneider et al. 2008; Steinacher et al. 2010; Tagliabue et al. 2010, Séférian et al, 2013).

Specific model configuration and tunings

The GLOBAL_REANALYSIS_BIO_001_029 simulation with the biogeochemical model PISCES is based on the version 3.6_STABLE of NEMO. It is forced offline by daily fields from the ocean dynamical simulation FREEGLORYS2V4 (described below in paragraph II.2) and from atmospheric reanalysis ERA-Interim (described below in paragraph II.3). There is no data assimilation (neither physical data nor biogeochemical data).

The horizontal grid is the standard ORCA025 tri-polar grid (1440 x 1021 grid points). The three poles are located over Antarctic, Central Asia and North Canada. The ¼ degree resolution corresponds to the equator. The vertical grid has 75 levels, with a resolution of 1 meter near the surface and 200 meters in the deep ocean.

The bathymetry used in the system is a combination of interpolated ETOPO1 (Amante and Eakins, 2009) and GEBCO8 (Becker et al., 2009) databases. ETOPO datasets are used in regions deeper than 300 m and GEBCO8 is used in regions shallower than 200 m with a linear interpolation in the 200 m –300 m layer. The minimum depth in the model is set to 12 m, except in the Bahamas region (~ 3 meters). Three islands have been added in the Torres Strait to diminish the transport and the Palk Strait (between India and Sri Lanka) has been opened with a 3 m depth.

A “partial cells” parameterization (Adcroft et al., 1997) is chosen to represent the topographic floor as staircases but making the depth of the bottom cell variable and adjustable to the real depth of the ocean floor (Barnier et al., 2006).

The GLOBAL_REANALYSIS_BIO_001_029 simulation is initialized in December 1991 with World Ocean Atlas 2013 for nitrate, phosphate, oxygen and silicate (Garcia et al., 2014a and b), with GLODAPv2 climatology including anthropogenic CO2 for Dissolved Inorganic Carbon and Alkalinity (Key et al., 2015) and, in the absence of corresponding data products, with model fields from a climatological run (4000 years) for dissolved iron and dissolved organic carbon. The other prognostic variables are initialized with uniform concentrations (low values). The end of year 1991 and year 1992 is considered as spin-up phase.

Nutrients are supplied to the ocean via three different external sources (Aumont et al., 2015): atmospheric deposition of Fe, Si, N and P, rivers and inputs of Fe from marine sediments. River supplies of biogeochemical parameters are co-located with freshwater inflows prescribed by the physical model.

Monthly atmospheric partial pressure of CO2 (global value) containing the anthropogenic emissions (Conway et al. 1994, Masarie et al, 1995) is prescribed at air-sea interface.

II.2 Ocean physics and sea ice forcings: FREEGLORYS2V4

Daily 3D temperature, salinity, velocity, vertical turbulent diffusivity (turbocline criteria) and 2D Mixed Layer Depth and Sea ice fraction are used to force the biogeochemical model PISCES. They come from the simulation FREEGLORYS2V4, which is the twin simulation of GLORYS2V4 reanalysis (GLOBAL_REANALYSIS_PHY_001_025) without data assimilation. It has been produced in Mercator-Ocean. The main features of this dynamical ocean are:



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NEMO 3.1

Atmospheric forcings from 3-hourly ERA-Interim reanalysis products, CORE bulk formulation

Vertical diffusivity coefficient is computed by solving the TKE equation

Tidal mixing has been parameterized following the work of Koch-Larrouy et al. (2008)

Sea-Ice model: LIM2 with the Elastic-Viscous-Plastic rheology

Initial conditions for temperature and salinity: EN4 monthly objective analyses combined with a regression model to rebuilt the December 1991 water masses conditions

The simulation starts at rest (cold start) in December 1991.

II.3 Atmospheric forcings: ERA-Interim

The atmospheric forcings are the same as those used to force the ocean physics FREEGLORYS2V4 but are daily averaged. The variables “wind speed at 10 m, net fresh water flux into ocean, net shortwave solar flux” from the ERA-Interim product are used to force PISCES model at air-sea interface.



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III VALIDATION FRAMEWORK

The products assessed are chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, phytoplankton carbon concentration, and primary production.

We scientifically assess the product quality by comparing systematically (for the whole time period) biogeochemical modeled fields (equatorial, Pacific ocean sections and surface maps) with available gridded data (chlorophyll from satellite ocean colour product) or WOA 2013 climatologies of nitrate, silicate, phosphate and dissolved oxygen (CLASS1). The WOA 2013 climatologies are based on observations from early 1900s to present. These visual comparisons are done on a monthly and annual basis. We moreover assess the timing, extension, magnitude and internannual variability of the North Atlantic bloom with monthly bias, median, 80th quantile and Hovmöller diagrams. We quantify the error with ocean colour chlorophyll by computing a RMS difference map. We monitor the model drift by plotting the annual time series of surface mean of chlorophyll, nitrate, phosphate, silicate, dissolved oxygen, iron, phytoplankton concentration and global vertically integrated primary production. It has to be noticed that we do not show any comparison of iron concentration (although it is provided to the user) because there is no climatology of observations to compare with. However we checked that our iron concentrations are in line with the results of Aumont et al. (2015). They compared (on their figure 8) the model with very sparse data of iron concentration in three vertical layers (0-50 m, 200-1000 m and 1000-5000 m).

NB: This methodology, although not perfect, allows to quantify the large scale and associated seasonal temporal scale biases and monitor the potential drifts of this product. This is not yet able to (or even designed to) provide accurate estimates of fine spatial and temporal scales. In the future, BGC-ARGO measurements will hopefully allow to improve the validation methodology, as well as the accuracy thanks to joint assimilation of ARGO and satellite Ocean Colour.

Variable Class Metric name Description Supporting observation

CHL 1 CHL-SURF-Y-CLASS1-SIMG-MEAN CHL-SURF-M-CLASS1-SIMG-RMSD

Map, equatorial section and meridional section in the Pacific Ocean of annual mean chlorophyll and RMSD on monthly mean chlorophyll in 1997 - 2017

Globcolour (ACRI) data at 25km resolution: Core dataset

CHL 3 CHL-SURF-M-CLASS3-SIMG-HOV CHL-SURF-M-CLASS3-SIMG-HOV

Hovmöller diagram in North Atlantic at 30°W

Globcolour (ACRI) data at 25 km resolution: Core Dataset

CHL 3 CHL-SURF-D-CLASS3-SIMG-MEDIAN CHL-SURF-D-CLASS3-SIMG-Q80

Global annual average times series Median and Quantile 80 of daily chlorophyll computed on North Atlantic (30-60°N; -80:0°E)


Table 3: Description of the different metrics and the associated observation datasets used in this report to assess the quality of the product. The metric nomenclature is described in the document CMEMS-PQ-Metrics-names-convention-v1.3.pdf (continues in next page).



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Variable Class Metric name Description Supporting observation

CHL 1 CHL-SURF-M-CLASS1-SIMG-BIAS-2017

Monthly global maps of Bias in year 2017


CHL 2 CHL-PROF-D-CLASS2-PROF-VISUAL Profiles at the BATS station in the Sargasso Sea for 1993-2017 period

BATS station, Steinberg et al., 2001

NO3 1 NO3-SURF-Y-CLASS1-CLIM-MEAN NO3-EQU-Y-CLASS1-CLIM-MEAN NO3-PAC-Y-CLASS1-CLIM-MEAN

Surface map, equatorial section and meridional section in the Pacific Ocean of annual mean nitrate

WOA 2013 (NODC) Garcia et al., 2014

NO3 2 NO3-PROF-D-CLASS2-PROF-VISUAL Profiles at the BATS station in the Sargasso Sea for 1993-2017 period

BATS station, Steinberg et al., 2001

PO4 1 PO4-SURF-Y-CLASS1-CLIM-MEAN PO4-EQU-Y-CLASS1-CLIM-MEAN PO4-PAC-Y-CLASS1-CLIM-MEAN

Surface map, equatorial section and meridional section in the Pacific Ocean of annual mean phosphate


SI 1 SI-SURF-Y-CLASS1-CLIM-MEAN SI-EQU-Y-CLASS1-CLIM-MEAN SI-PAC-Y-CLASS1-CLIM-MEAN

Surface map, equatorial section and meridional section in the Pacific Ocean of annual mean silicate


O2 1 SI-200m-Y-CLASS1-CLIM-MEAN SI-EQU-Y-CLASS1-CLIM-MEAN

SI-PAC-Y-CLASS1-CLIM-MEAN

200m depth map, equatorial section and meridional section in the Pacific Ocean of annual mean oxygen


PP 1 BGC-CLASS1-PRIMARY_PRODUCTION

Map, equatorial section and meridional section in the Pacific Ocean of annual mean of vertically integrated primary production in 2011

Standard VGPM algorithm product Behrenfeld and Falkowski, 1997), MODIS sensor

All 3 BGC-SURF-Y-CLASS3-MEAN Global annual integrated times series

Table 4: Description of the different metrics and the associated observation datasets used in this report to assess the quality of the product. The metric nomenclature is described in the document CMEMS-PQ-Metrics-names-convention-v1.3.pdf



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IV VALIDATION RESULTS

IV.1 Chlorophyll

IV.1.1 Climatological biases

Figure 2 shows a comparison between model and observations (Globcolour product) of annual surface chlorophyll computed on years 1998 – 2017. Modelled mean annual chlorophyll-a field shows a good agreement with satellite-derived estimates at the global scale. The large-scale structures i.e. the main biogeographic provinces of Longhurst et al. (1998) (e.g. double-gyres, Antarctic Circumpolar Current, tropical band, Eastern Boundary Upwellings) are well reproduced. Let us now go into details.

Figure 2: (top) Annual mean of surface chlorophyll averaged over the period 1998-2017 (mg.m-3). (left) model; (right) Globcolour observations NB: the annual means are done on the same number of samples.. (bottom-left) RMS difference

computed with monthly chlorophyll means on the period 1997 – 2017. Colorbar is in log scale; (bottom-right) mean difference (model minus obs) over the period 1997-2017 (mg.m-3).

In the tropical band of the Pacific Ocean, the model correctly reproduces the westward pointing “arrowhead” shape of the chlorophyll tongue. However, the tongue is slightly too wide, expands too westwards and its south/north limits are too diffuse. This can be seen on Figure 2 (bottom-right) where there is a positive anomaly on the border of the tropical tongue. In the Tropical Atlantic, the model reproduces well the equatorial tongue of chlorophyll with an increase of chlorophyll in boreal summer (Grodsky et al, 2008) in conjunction of the equatorial upwelling intensification (not shown). But it fails at reproducing the chlorophyll maxima in North Equatorial Countercurrent (between 8°N and 10°N) formed by the retroflecting of North Brazil Current (Fratantoni et al., 2002), entraining nutrients eastward from the Amazon mouth. It can be seen on Figure 2 and Erreur ! Source du renvoi introuvable. with the negative anomaly (especially from July to November). The plume of the Congo river is also underestimated (maybe due to a lack of nutrient inputs at the river mouth).



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In the Indonesian region, the model fails at reproducing the coastal maxima (around the islands) (Figure 2).

In the Indian Ocean, the model slightly underestimates the chlorophyll-a levels all over the year and in particular around India and in the Bay of Bengal. Even if it is less extended, the model reproduces the summer bloom (due to a coastal upwelling) along the Somalia and the Arabian Peninsula (Yemen and Oman) in July-August-September but not the winter bloom along the Pakistan coast in January-February-March (not shown; see Lévy et al., 2007 and Koné et al, 2009 for a description of these blooms).

The subtropical gyres are slightly less oligotrophic (Figure 2) and in North Atlantic, the southern boundary of the productive region is not enough zonal (dipole between 30 and 40°N in North Atlantic on Figure 2 bottom-right).

The Eastern Boundary Upwelling Systems are present but are not productive enough (Figure 2).

In the southern high latitudes (beyond 30°S), the model overestimates chlorophyll concentrations all year long (Figure 2 and Figure 4) except in ‘hot’ spots where chlorophyll is underestimated.

IV.1.2 Seasonal cycle and its interannual variability

Figure 3: Monthly chlorophyll bias at sea surface (Log10(model) minus Log10(observations)) in year 2017 (no units) (continues in next page).



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Figure 4: Monthly chlorophyll bias at sea surface (Log10(model) minus Log10(observations)) in year 2017 (no units)

In Mid and High latitudes of northern hemisphere, there is a time lag between the seasonal cycle of the model and the one observed in data (Figure 4). Let us study now the North Atlantic spring bloom onset. Figure 5 and Figure 6 are Hovmöller diagrams at 30°W in North Atlantic (20°S-70°N) respectively for the year 2017 and for the whole interannual time series (1998 – 2017). They show that the model is able to reproduce the main features of the seasonal cycle: a bloom in spring when the mixed layer, rich in nutrients, shoals (light limitation); a decrease of chlorophyll concentration in summer due to a thin mixed layer very poor in nutrients (nutrient limitation); a secondary bloom in autumn when the mixed layer is deepening and nutrients are entrained within it; and in winter, a period of weak



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production (light limitation). However, Figure 5 and Figure 6 also show that the spring bloom onset is too early in the model. Moreover, the modelled bloom does not maintain enough in July-August-September at mid-latitudes (45°N – 60°N) and propagates too rapidly towards high latitudes. Figure 7 represents the median and percentile 80 computed on a mid-latitude box between 30°N and 60°N for model (blue) and data (red). Median and Percentile 80 are used to give robust estimates of mean and maximum filtered from extreme values over a given region. This figure shows that the conclusions drawn from Figure 6 at 30°W are valid in the entire North Atlantic basin. These significant differences between model and data have several possible explanations:

- The light limitation: phytoplankton species begin their development with too low levels of light so the bloom begins too early.

- There are not enough nutrients in the mixed layer or they are consumed too rapidly so the bloom does not maintain during summer.

- The zooplankton species are not controlling enough phytoplankton growth to prevent the bloom from beginning in February-March and to allow the bloom to carry on in summer period.

The reasons for the bloom onset observed in ocean colour data are still under debate among the scientific community. The critical depth hypothesis of Sverdrup et al. (1953) attributed the bloom onset to the mixed layer shoaling and considered constant loss rates (mortality and grazing). This hypothesis is based on a bottom-up control of the bloom onset but it has been recently criticized. Behrenfeld et al. (2010, 2013) proposed a dilution-recoupling hypothesis based on a top-down control. Taylor and Ferrari (2011) assumed that the bloom is triggered by the shutdown of the turbulent mixing.

All these regional biases of the model are synthesized with the map of RMS difference on Figure 2.



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Figure 5: Hovmöller diagram of surface chlorophyll concentration in 2017 computed with daily mean fields. (top) model; (bottom) Globcolour L3 observations



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Figure 6: Hovmöller diagram of surface chlorophyll concentration on 1997-2017 period computed with monthly mean fields. (top) model; (bottom) Globcolour observations

Figure 7: Daily median (dashed line) and percentile 80 (plain line) of surface chlorophyll concentration (mg.m-3) in North Atlantic (30-60°N; 80°W-0°W) for year 2017. (blue) model; (red) observations



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IV.1.3 Vertical distribution

To give a vertical view of chlorophyll distribution, we plot on Figure 8 an equatorial and Pacific section of the model, although there is no climatological data yet to compare them with. The reader can see the vertical extension of oligotrophic gyres and the deep chlorophyll maximum in subsurface layers. We complete the assessment by comparing the vertical profiles of chlorophyll at the BATS (Bermuda Atlantic Time Series) station in the Sargasso Sea (Steinberg et al., 2001) situated in the North Atlantic subtropical gyre. We see that the model is able to predict the Subsurface Chlorophyll Maximum (SCM). But it is slightly shifted vertically and the chlorophyll concentrations are higher than in BATS data. The model catches the seasonal cycle with a bloom during the deepening of the mixed layer in Winter. The model fails at reproducing the good magnitude of the bloom. In Summer, the production in the mixed layer is more limited and is mainly due to the local remineralization of organic matter (regenerated production). The Figure 13 (in Nitrate section) shows that these differences in vertical chlorophyll profile seem to be due to a too shallow nitracline.

Figure 8: Annual section of modelled chlorophyll (mg.m-3); (left) Equatorial section; (right) Section in Pacific at 155°W



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Figure 9: Depth-time diagram of chlorophyll concentration (mg.m-3

) in the layer 0-400 m at BATS station. (top) model; (bottom) Bottle data at BATS station.

IV.2 Nitrate

Figure 10 shows a comparison at sea surface of nitrate climatology derived from the World Ocean Atlas 2013 (WOA) and from the model on the period 1993-2017. Globally, there is a good agreement between model and climatology except in the Southern Ocean where nitrate levels are too strong, and along the Arabian Peninsula (Yemen and Oman) and in the bay of Bengal where nitrates are absent in the model.

Due to the trade wind dragging surface water westward, deep nutrient-rich water is upwelled in the eastern part of the Pacific basin. Equatorial divergence then spreads it westward along the equator and north- and southward from the equator (Coriolis effect). This is the reason for the arrowhead shape of the chlorophyll and nitrate tongue in the Pacific Ocean (Figure 2 and Figure 10). In the Atlantic and Indian Ocean, nitrate is entirely consumed by phytoplankton. Figure 11 shows the



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equatorial section of nitrates for the model and in WOA 2013. The slope of nitrate isolines (reflecting the equatorial upwelling) are well reproduced.

Figure 12 shows a Pacific section of nitrate concentration in model compared to WOA 2013 climatology. Here again main structures such as the subtropical gyres are well reproduced by model. Nonetheless, the stronger equatorial upwelling can still be seen near the surface.

On Figure 13, we see that the nitracline at BATS station is too shallow and too diffuse. BATS station is situated in the western North Atlantic subtropical gyre, bounded on the northwest by the Gulf Stream (Steinberg et al., 2001). We hypothesize that this boundary is slightly shifted southward to explain this difference in nitrate profiles at the BATS site. Furthermore, we also see on Figure 13 that a model drift is still ongoing at the end of the simulation (uplift of the nitracline).

Figure 10: Annual mean of nitrate concentration at sea surface (mmol.m-3

). (left) 1993-2017 model climatology ; (right) World Ocean Atlas 2013

Figure 11: Annual section of nitrate concentration at the equator (mmol.m-3

). (left) 1993-2017 model climatology; (right) World Ocean Atlas 2013



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Figure 12: Annual section of nitrate concentration at 155°W in the Pacific Ocean (mmol.m-3


Figure 13: Depth-time diagram of nitrate concentration (mmol.m-3




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IV.3 Phosphate

Figure 14, Figure 15 and Figure 16 show respectively climatological phosphate distribution at sea surface, at equator and at 155° in the Pacific Ocean, compared with WOA 2013. The same conclusions as in the previous nitrate section can be drawn from these figures. We can notice moreover that phosphate concentration in Indian and South Pacific Ocean is slightly too low.

Figure 14: Annual mean of phosphate concentration at sea surface (mmol.m

-3). (left) 1993-2017 model climatology ; (right)

World Ocean Atlas 2013

Figure 15: Annual section of phosphate concentration at the equator (mmol.m

-3). (left) 1993-2017 model climatology;

(right) World Ocean Atlas 2013

Figure 16: Annual section of phosphate concentration at 155°W in the Pacific Ocean (mmol.m

-3). (left) 1993-2017 model

climatology; (right) World Ocean Atlas 2013



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IV.4 Silicate

Figure 17, Figure 18 and Figure 19 show respectively silicate distribution at sea surface, at the equator and at 155° in the Pacific Ocean, compared with WOA 2013. The large-scale distribution is well reproduced by the model except in the southern ocean (Ross Sea, Weddell Sea) where the concentrations are too high.

Figure 17: Annual mean of silicate concentration at sea surface (mmol.m-3


Figure 18: Annual section of silicate concentration at the equator (mmol.m-3




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Figure 19: Annual section of silicate concentration at 155°W in the Pacific Ocean (mmol.m-3


IV.5 Dissolved oxygen

Figure 20, Figure 21 and Figure 22 show respectively climatological dissolved oxygen concentration at 200 m depth, at the equator and at 155° in the Pacific Ocean, compared with WOA 2013. The oxygen concentration at large-scale is well reproduced by the model and in particular the Oxygen Minimum Zones (OMZ) where oxygen is severely depleted.

On Figure 23, we see that the oxygen levels are well reproduced in the 0-100 m layer. Each winter the deep mixed layer enriches in oxygen. In summer the ventilated layer is trapped under the thin mixed layer (where mainly remineralised production occurs). The model seems to correctly predict the mixed layer deepening/shallowing processes. But in the deep ocean, the minimum oxygen layer is thicker in the model. This may be the consequence of the excessive production in the 0-200 m layer in the model.

Figure 20: Annual mean of dissolved oxygen concentration at 200 m depth (mmol.m-3


Figure 21: Annual section of dissolved oxygen concentration at the equator (mmol.m-3




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Figure 22: Annual section of dissolved oxygen concentration at 155°W in the Pacific Ocean (mmol.m-3


Figure 23: Depth-time diagram of dissolved oxygen concentration (mmol.m-3




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IV.6 Phytoplankton concentration in carbon

Figure 24 and Figure 25 show climatological phytoplankton concentration (expressed in carbon) at sea surface, at the equator and at 155° in the Pacific Ocean. For the time being, there is no global dataset to compare with.

Figure 24: Annual mean of phytoplankton concentration in carbon at sea surface (mmol.m-3

) : 1993-2017 model climatology

Figure 25: Section of phytoplankton concentration (mmol.m-3

) for a 1993-2017 model climatology at the equator (left) and in the Pacific Ocean at 155°W (right)

IV.7 Iron

Figure 26 and Figure 27 show climatological iron concentration at sea surface, at the equator and at 155° in the Pacific Ocean. For the time being, there is not enough data to compare with. We however checked that our iron concentrations are in line with the results of Aumont et al. (2015). They compared (on their Figure 8) model with very sparse data of iron concentration in three vertical layers (0-50 m, 200-1000 m and 1000-5000 m).



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Figure 26: Annual mean of iron concentration at sea surface (nmol.m-3

) : 1993-2017 model climatology

Figure 27: Section of iron concentration (nmol.m-3

) for a 1993-2017 model climatology at the equator (left) and in the Pacific Ocean at 155°W (right)



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IV.8 Primary production

Figure 28 shows different estimations of vertically integrated primary production in year 2014. We can see that there are a lot of discrepancies between the different observation-based estimates.. In comparison to the VGPM algorithm, the model seems to over-estimate the production in the tropical band and to under-estimate in high latitudes. The model is closer to the other satellite-based estimates with the Tropical region being the most productive and the high-latitudes being far less productive.

Figure 28: Annual mean of vertically integrated primary production (mgC m-2

day-1

) for the year 2014; (top-left) model; (top-right) Modis satellite product based on VGPM algorithm (Behrenfeld and Falkowski, 1997); (bottom-left) Eppley algorithm; (bottom-right) CBPM algorithm (see website http://www.science.oregonstate.edu/ocean.productivity/ )

IV.9 Surface partial pressure of carbon dioxide

Figure 29 shows the comparison between the model and the product MULTIOBS_GLO_BIO_REP_015_005 (based on the observation database SOCAT v2 and a Feed-Forward Neural Network). We see that the model is able to reproduce the large-scale structures (the tropical tongue, the high values in the Eastern Boundaries upwellings. We have chosen to show one particular year (2016) instead of a climatology because there is a global and significant trend due the anthropogenic CO2 emissions (that are partly stored in the ocean). See section 1.7 of Ocean State Report 2 for more explanations (Von Schuckmann et al., 2018).

http://www.science.oregonstate.edu/ocean.productivity/



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Figure 29: Annual mean of surface partial pressure of carbon dioxide in year 2016; (left) model; (right) MULTIOBS_GLO_BIO_REP_015_005 product

IV.10 pH

Figure 30 shows the comparison between the model and the product MULTIOBS_GLO_BIO_REP_015_005. The model reproduces the large-scale patterns with for instance a more acid tropical tongue but it presents a generalized bias: it is globally too acid. We see on Figure 31 that the model has the good trend but has a bias of 0.02.

Figure 30: Annual mean of surface pH in year 2016; (left) model; (right) MULTIOBS_GLO_BIO_REP_015_005 product

Figure 31: Global annual time series of pH; (blue) MULTIOBS_GLO_BIO_REP_015_005 product; (red) model



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IV.11 Global balances

Figure 32 is the global temporal evolution of all variables disseminated in the product GLOBAL_REANALYSIS_BIO_001_029. It covers the whole period of simulation including the year 1992 that is not disseminated by CMEMS (considered as spin-up). It shows that although the model has not yet reached a steady state, the values of global annual phytoplankton carbon fixation rate (primary production) are in the range of global estimations based on satellite observations (30 to 50 Gt in Longhurst et al., 1995; 48 Gt in Field et al., 1998). The ranges of variation of nitrate, silicate and chlorophyll are similar to those obtained by Yool et al. (2011, 2013) with the ecosystem model of intermediate complexity MEDUSA.

Figure 32: Global annual time series over period 1992 - 2018: (left) Surface mean concentration of Chlorophyll, Nitrate and Phytoplankton biomass in carbon, Integrated Primary Production; (right) Surface mean concentration of Phosphate, Silicate, Oxygen and Iron



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V SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES

Date Change/Event description System version other



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VI QUALITY CHANGES SINCE PREVIOUS VERSION

The quality of this product is slightly improved since previous version. The equatorial tongue in the Pacific expands less westward and is less wide zonally (Figure 33 and Figure 34). As N2 fixation has been added in the model, the oligotrophic gyres are less depleted in chlorophyll.

Besides, there are technical improvements. We use a more up-to-date version of NEMO-PISCES (version 3.6_STABLE, same as in Aumont et al., 2015). Daily mean fields are available. We provide the product on a regular grid. This is more consistent with physical model outputs.

Figure 33: (top) Annual mean of surface chlorophyll over year 2016 (mg.m-3). (middle) new version; (bottom) old version; (bottom) Globcolour observations. Colorbar is in log scale.



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Figure 34: (top) Annual mean of surface nitrate over year 2016 (mg.m-3). (left) new version; (right) old version; (bottom) WOA 2013.Figure 32



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VII REFERENCES

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Documents

Global Production Centre GLOBAL REANALYSIS BIO 001 029 · QUID for Global Ocean Biogeochemistry Hindcast GLOBAL_REANALYSIS_BIO_001_029 Ref: Date: Issue: CMEMS-GLO-QUID-001-029 03