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QUALITY INFORMATION DOCUMENT

For Global Biogeochemical Analysis and Forecast Product

GLOBAL_ANALYSIS_FORECAST_BIO_001_014

Issue: 3.0

Contributors: Perruche C., Hameau A., Paul J., Régnier C., Drévillon M.

Approval Date by Quality Assurance Review Group : October 2016

QUID for Global Biochemical Analysis and Forecast Product


Ref: CMEMS-GLO-QUID-001-014

Date : 15 September 2016

Issue : 3.0

© EU Copernicus Marine Service – Public / 38

CHANGE RECORD

Issue Date § Description of Change Author Validated By

1.0 13/05/2014 All Creation of the document Perruche, C. Drillet, Y.

1.1 29/09/2014 All Modified after the QuARG review Perruche, C. Drillet, Y.

2.0 15/12/2014 § IV.2 mostly

Forecast assessment Perruche, C. Drévillon, M.

2.1 17/12/2014 MyOF modifications Y. Drillet Y. Drillet

2.2 13/02/2015 Modified after the QuARG review C. Perruche C. Perruche

2.3 May 1 2015 all Change format to fit CMEMS graphical rules

L. Crosnier

2.4 February 2016

All Remove old MyOcean references M Drevillon Y Drillet

3.0 September

2016

§VI mostly

New release forced by global_analysis_forecast_phys_001_024

J. Paul and C. Perruche

Y. Drillet





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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 .................................................................................................................... 4

II Production Subsystem description ................................................................................................................ 6

II.1 Biogeochemical model PISCES ................................................................................................................ 6

II.2 Physical model NEMO .............................................................................................................................. 7

II.2.1 Until October 19th

2016 ..................................................................................................................... 7

II.2.2 From October 19th

2016 on ............................................................................................................... 7

II.3 Coupling and configuration ...................................................................................................................... 8

III Validation framework ............................................................................................................................... 9

IV Validation results ......................................................................................................................................... 11

IV.1 Hindcast assessment ............................................................................................................................... 11

IV.1.1 Chlorophyll ................................................................................................................................. 12

IV.1.2 Nitrates ........................................................................................................................................ 19

IV.1.3 Phosphates .................................................................................................................................. 21

IV.1.4 Silicate ........................................................................................................................................ 22

IV.1.5 Dissolved Oxygen ....................................................................................................................... 23

IV.1.6 Iron .............................................................................................................................................. 25

IV.1.7 Phytoplankton biomass in carbon ............................................................................................... 25

IV.1.8 Primary production ..................................................................................................................... 26

IV.2 Forecast assessment ................................................................................................................................ 27

V Quality changes since previous version ...................................................................................................... 29

VI NEW FORCING FIELDS SINCE OcTober 2016 ..................................................................................... 33

VII References ............................................................................................................................................... 37





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I EXECUTIVE SUMMARY

I.1 Products covered by this document

The product described in this document is: GLOBAL_ANALYSIS_FORECAST_BIO_001_014. It is a near real time and forecast product updated weekly.

It consists of weekly means of several biogeochemical variables (Nitrates, Phosphates, Silicates, Iron, Dissolved Oxygen, Concentration of Chlorophyll, Phytoplankton Biomass, and Primary Production) on a regular grid at ½° resolution (0.5° lat x 0.5° lon), with 50 vertical levels, on the global ocean.

I.2 Summary of the results

The quality of the global biogeochemical system has been assessed using a seven-year hindcast (years 2007-2013). For bias computation, we adopt the following convention: model – observation. 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, concentrations are still too high in the tropical band. Concerning the temporal monitoring, our model succeeds well in reproducing the seasonal cycle at mid- and high- latitudes (spring bloom), but the timing of the bloom is not yet in phase with that of observations (the modelled bloom is too early, namely one or two-month lag). On the global ocean, the model has a correlation of 0.59 in log10(chlorophyll) at the sea surface in comparison with satellite chlorophyll observations. Real time one-week forecasts display some skill with respect to the persistence of the hindcast. However, users must keep in mind that both the hindcast and forecast products still bear large uncertainties with respect to climatologies and observations.

Nutrients (NO3, PO4, Si): Concentration of nutrients are in good agreement with World Ocean Atlas Climatology at global scale (correlation > 0.9). The concentrations of nutrient are globally too high at sea surface in the model (positive mean bias). This is mainly due to the tropical band where the model overestimates the nutrient concentrations.

Dissolved Oxygen: Oxygen presents very good scores at the sea surface (correlation > 0.9; in comparison with World Ocean Atlas climatology). This is due to the intrinsic link between O2 concentration and temperature (and especially at sea surface). The modelled dissolved oxygen therefore benefits from the assimilation of temperature data. In subsurface and deep layers, the model is able to reproduce OMZs (oxygen minimum zone).

I.3 Estimated Accuracy Numbers

Estimated Accuracy Numbers are given in Table 1 for year 2013, which is the last year of the hindcast simulation. They are computed from monthly fields. Model outputs are interpolated on the regular





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grid of data. The monthly model fields are masked with monthly data and inversely, so that the annual mean is computed from the same number of samples. Estimated Accuracy Numbers are computed by following conventions laid out in the MyOcean Cal/Val guidelines document (MyO2-PQ-guidelines-phase2). The observation products used for these statistics are described in Table 2.

Variable RMS difference (new model)

RMS difference

(old model)

Mean difference (new model - obs)

Mean difference (old

model - obs)

Chlorophyll 0.4386 mg/m3 0.431 mg/m3 0.0048 mg/m3 0.338 mg/m3

Log10(chlorophyll) 0.317 0.375 0.0133 0.0874

Dissolved Oxygen 11.24 µmol/L 11.99 µmol/L 3.13 µmol/L 3.16 µmol/L

Nitrates 3.70 µmol/L 3.40 µmol/L 1.37 µmol/L 0.32 µmol/L

Phosphates 0.307 µmol/L 0.291 µmol/L 0.136 µmol/L 0.053 µmol/L

Silicates 10.47 µmol/L 5.83 µmol/L

Table 1: Root Mean Square Error and Mean Error <(mod – obs)> calculated on monthly fields at sea surface in 2013.





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II PRODUCTION SUBSYSTEM DESCRIPTION

The product GLOBAL_ANALYSIS_FORECAST_BIO_001_014 is provided by the production unit MERCATOR Océan of the production centre MFC-GLOBAL.

The product GLOBAL_ANALYSIS_FORECAST_BIO_001_014 is a global biogeochemical simulation forced off-line by daily fields provided by a physical simulation PSY3V3R3 (described hereafter and in Lellouche et al., 2013) until October 2016. From October 19th 2016, it is forced by the Copernicus product GLOBAL_ANALYSIS_FORECAST_PHYS_001_024, which is run at a resolution of 1/12° and is degraded offline to ¼° to force the biogeochemical model.

II.1 Biogeochemical model PISCES

The biogeochemical model used is PISCES (Aumont, in prep). It is a model of intermediate complexity designed for global ocean applications (Aumont and Bopp, 2006) and is part of the NEMO modeling platform (Madec et al. 2008). 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 the 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 simulates 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 and denitrification.

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 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). The biogeochemical model is initialized with the World Ocean Atlas 2001 for nitrate, phosphate, oxygen and silicate (Conkright et al. 2002), with GLODAP climatology including anthropogenic CO2 for Dissolved Inorganic Carbon and Alkalinity (Key et al. 2004) and, in the absence of corresponding data products, with model fields for dissolved iron and dissolved organic carbon. Boundary fluxes account for nutrient supply from three different sources: atmospheric deposition (Aumont et al., 2008), rivers for nutrients, dissolved inorganic carbon and alkalinity (Ludwig et al., 1996) and inputs of Fe from marine sediments.





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II.2 Physical model NEMO

II.2.1 Until October 19th 2016

The physical ocean model is the Mercator-Ocean system PSY3R3V3 at ¼° resolution (Lellouche et al., 2013). The main features of this dynamical ocean are:

- NEMO 3.1 – ¼° - 50 levels

- Atmospheric forcing from 3-hourly ECMWF analysis products, CORE bulk formulation.

- Vertical diffusivity coefficient is computed by solving the TKE equation.

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

- Initial conditions: Levitus 2005 climatology for temperature and salinity. Ifremer/Cersat data for sea ice concentration and GLORYS2V1 for sea ice thickness.

- Data assimilation scheme: SAM2V1 (Kalman filter with SEEK formulation) + 3D-Var biases correction in temperature and salinity for the slowly evolving large-scale, both with Incremental Analysis Update.

- Data assimilated: Sea Surface Temperature (Reynolds AVHRR-AMSR 1/4°); Sea Surface Height (Jason2, Cryosat, Saral); InSitu temperature and salinity vertical profiles from Coriolis Center with Extra Quality control; hybrid MSSH.

See for more details Lellouche et al. (2013).

II.2.2 From October 19th 2016 on

The physical model of ocean is the Copernicus product GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 coarsened at ¼° resolution. The main features of this dynamical ocean are:

- NEMO 3.1 –1/12°° - 50 levels.

- Atmospheric forcing from 3-hourly ECMWF analysis products, CORE bulk formulation.

- Vertical diffusivity coefficient is computed by solving the TKE equation.

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

- Data assimilation scheme: SAM2V1 (Kalman filter with SEEK formulation) + 3D-Var biases correction in temperature and salinity for the slowly evolving large-scale, both with Incremental Analysis Update.

- Data assimilated: Sea Surface Temperature (CMEMS OSTIA) Sea Surface Height (all available missions); InSitu temperature and salinity vertical profiles from CMEMS with Extra Quality control; sea ice concentration (from CMEMS); hybrid MSSH.

See for more details in the QUID and PUM documents for product GLOBAL_ANALYSIS_FORECAST_PHYS_001_024





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II.3 Coupling and configuration

The biogeochemical model PISCES (NEMO3.2) is forced offline by daily fields. A special treatment is done on the vertical diffusivity coefficient (Kz): the daily mean is computed from Log10(Kz) values after a filtering of enhanced convection (Kz increased artificially to 10 m2.s-1 when the water column is unstable). The purpose of this Log10 is to average the orders of magnitudes and to give more weight to small values of vertical diffusivity.

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 50 levels, with a resolution of 1 meter near the surface and 500 meters in the deep ocean.

The biogeochemical simulation starts in January 2007. Outputs are interpolated on a standard collocated grid at 1/2 degree.





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III VALIDATION FRAMEWORK

We assess the system performance and the associated product quality by comparing systematically (near real time) biogeochemical modeled fields (equatorial and Pacific sections, and surface maps) with available data or climatologies (CLASS1 metrics). These eyeball comparisons are done on a monthly and annual basis. In delayed time, we mainly monitor the seasonal cycle of chlorophyll and compare statistics time series (median, 80th percentile, RMS misfit, Taylors and Hovmöller diagrams). We finally monitor the model drift by plotting the time series of global vertically integrated primary production and averages of the main variables. It has to be noticed that we did not assess the iron concentration (although it is provided to the user) because there is no climatology to compare with.

The validation methodology and metrics classification are described in the MyOcean Cal/Val guidelines document (MyO2-PQ-guidelines-phase2). Table 2 summarizes the type of metrics used to monitor the system.

Variable Class Metric name Description Supporting observation

CHL 1 BGC-CLASS1_CHL_MEAN Map, equatorial section and meridional section in the Pacific Ocean of annual mean chlorophyll, bias and RMSE of monthly mean chlorophyll in 2013

Globcolour (ACRI) data at 25km resolution

CHL 2 BGC_CLASS2_CHL Hovmöller diagram in North Atlantic at 20°W

Globcolour (ACRI) data at 25 km resolution

CHL 3 BGC-CLASS3-CHL_REGIONAL Global annual average times series Median and Percentile 80 of monthly chlorophyll computed on North Atlantic (30-60°N; -80:0°E) in 2013


CHL 3 BGC-CLASS3-CHL Taylor diagram for year 2013 (correlation on monthly surface fields)


NO3 1 BGC-CLASS1-NITRATE_REGIONAL Map, equatorial section and meridional section in the Pacific Ocean of annual mean nitrate in 2013

WOA 2009 (NODC) Garcia, 2010a

NO3 3 BGC-CLASS3-NO3 Taylor diagram for year 2013 (correlation on monthly surface fields)


PO4 1 BGC-CLASS1-PHOSPHATE_REGIONAL

Map, equatorial section and meridional section in the Pacific Ocean of annual mean phosphate in 2013


PO4 3 BGC-CLASS3-PO4 Taylor diagram for year 2013 (correlation on monthly surface fields)


Si 1 BGC-CLASS1-SILICATE_REGIONAL Map, equatorial section and meridional section in the Pacific Ocean of annual mean silicate in 2013


Si 3 BGC-CLASS3-SI Taylor diagram for year 2013 (correlation on monthly surface fields)






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O2 1 BGC-CLASS1-OXYGEN_REGIONAL Map, equatorial section and meridional section in the Pacific Ocean of annual mean oxygen in 2013

WOA 2009 (NODC) Garcia, 2010b

O2 3 BGC-CLASS3-O2 Taylor diagram for year 2013 (correlation on monthly surface fields)

WOA 2009 (NODC) Garcia, 2010b

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 2013

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

PP 3 BGC-CLASS3-PRIMARY_PRODUCTION_REGIONAL

Global annual integrated times series

Table 2: List of metrics that were computed to assess the system.





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IV VALIDATION RESULTS

IV.1 Hindcast assessment

Biogeochemical data to assess the quality of biogeochemical models are still scarce. This biogeochemical system is evaluated by systematically comparing model fields to observations (when it is possible) or climatologies at global scale. From now on, we will present either diagnostics on the last year of the hindcast simulation, namely year 2013, or diagnostics on the whole period of the simulation, namely 2007 – 2013.

Table 3 gives means and standard deviations over the global domain of chlorophyll, dissolved oxygen, nitrates, phosphates and silicates for model and data (in 2013).

Variable Observations New Model Old Model

Chlorophyll Mean = 0.198 mg/m3

Std = 0.412 mg/m3

Mean = 0.203 mg/m3

Std = 0.458 mg/m3

Mean = 0.231 mg/m3

Std = 0.349 mg/m3

Log10( Chlorophyll ) Mean = -0.944

Std = 0.397

Mean = -0.931

Std = 0.400

Mean = -0.856

Std = 0.418

Dissolved oxygen Mean = 250.78 µmol/L

Std = 53.61 µmol/L

Mean = 254.33 µmol/L

Std = 53.76 µmol/L


Std = 53.75 µmol/L

Nitrates Mean = 5.235 µmol/L

Std = 8.337 µmol/L


Std = 8.245 µmol/L


Std = 7.505 µmol/L

Phosphates Mean = 0.533 µmol/L

Std = 0.560 µmol/L


Std = 0.556 µmol/L


Std = 0.512 µmol/L

Silicates Mean = 7.66 µmol/L

Std = 14.03 µmol/L

Mean = 13.2 µmol/L

Std = 15.57 µmol/L


Std = 17.63 µmol/L

Table 3: Mean and standard deviation values over the global domain in 2013 at sea surface computed on monthly fields for model and observations on a regular grid. The monthly model

fields are masked with monthly data and inversely.





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Figure 1: Globally averaged surface chlorophyll (green), nitrate (blue), phytoplankton biomass in carbon (red) and integrated primary production (magenta) as a function of time (years) in

GLOBAL_ANALYSIS_FORECAST_BIO_001_014.

Figure 1 shows the temporal evolution of surface chlorophyll, nitrate, phytoplankton biomass and integrated primary production, during the whole simulation period. During these 7 years of simulation, we observe the stabilisation of the model, which reaches a quasi-steady state around 2011.

IV.1.1 Chlorophyll

Figure 2 shows a comparison of annual chlorophyll at sea surface in 2013 between the model (left) and the Globcolour product (right) in log scale. The monthly model fields are masked with monthly data, thus the annual mean is computed from the same number of samples. The modelled mean annual chlorophyll field shows a good agreement with satellite derived estimates at the global scale.

Large scale structures i.e. the main biogeographic provinces of Longhurst et al. (1998) (e.g. double-gyres, Antarctic Circumpolar Current, the tropical band, Eastern Boundary Upwellings) are well reproduced.





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The main oligotrophic gyres are well localised (Figure 2), with very low values of surface chlorophyll (< 0.05 mg.m-3). They are associated with a chlorophyll maximum in subsurface which is located between 100 and 250m depth (see Figure 3).

The important production of the North Atlantic region is well represented (see Figure 2).

Main eastern boundary upwelling systems (Benguela, California, Humbolt, and Canary current systems) are correctly modelled too (see Figure 2).

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 and becomes shallower than the euphotic layer; a decrease of chlorophyll concentration in summer due to a thin mixed layer very poor in nutrients (nutrient limitation); a second bloom in autumn when the mixed layer deepens and nutrients are entrained at its base; and in winter, a period of weak production (due to light limitation) (see Figure 5).

Concerning the interannual variability of the seasonal cycle, we can see on Figure 6 and Figure 7 that the model succeeds in reproducing the interannual variability of the chlorophyll concentration in the North Atlantic. For example, in the Canary current upwelling at 20°N, positive anomalies in 2007, 2009 and 2012 are present in the model. The model also correctly predicts a mid-latitude spring bloom that began more southerly than usual in 2009 and 2012.

However, there are some discrepancies with ocean colour data.

The subtropical gyres are slightly too oligotrophic and in the North Atlantic, the boundary between oligotrophic waters and the the northern productive region is not zonal enough (see Figure 2 and Figure 8).

Eastern Boundary Upwelling Systems are not productive enough (see Figure 4 and Figure 8).

The model overestimates the concentration of chlorophyll (see Figure 8) in the tropical band (Pacific, Indian and Atlantic oceans), and the westward “arrowhead” shape of the chlorophyll tongue in the Pacific spreads too much westward (see Figure 2).

In the Indian Ocean, the model overestimates the chlorophyll levels, except in the Arabian Sea.

The model overestimates in average chlorophyll concentrations in the Northern High latitude region (see Figure 2) and underestimates it in the Southern High latitude one (see Figure 4 and Figure 8).

In the North Atlantic, the bloom starts too early (one or two months, see Figure 5 and Figure 6), and it does not persist in summer as much as in data (see Figure 5). Moreover, it is too productive (see Figure 5).

The reasons of 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 has been recently criticized. Behrenfeld et al. (2010, 2013) proposed a dilution-recoupling hypothesis based on a top-down





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control. Taylor and Ferrari (2011) assumed that the bloom is triggered by the shutdown of the turbulent mixing.

Figure 2: Annual mean of surface chlorophyll concentration in 2013 (mg.m-3); (left) model; (right) Globcolour data.

Figure 3: Section of modelled chlorophyll concentration (mg.m-3) at the equator (left) and in the Pacific Ocean at 155°W (right).





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Figure 4: RMS Misfit for chlorophyll over year 2013, calculated from monthly data and model outputs at the sea surface.

Figure 5: Median (left) and Percentile 80 (right) of chlorophyll concentration (mg.m-3) in the North Atlantic (30-60°N; 80°W-0°W) for year 2013; (blue) old version of the model; (green) current

version of the model; (red) data.





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Figure 6: Hovmöller diagram of Log10( chlorophyll ) between 2007 and 2013 at 20°W in the North Atlantic (20°S:70°N); (left) model; (right) Globcolour data.





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Figure 7: Hovmöller diagram of the chlorophyll anomaly (Log10(monthly mean) – Log10(mean over the whole period)) between 2007 and 2013 at 20°W in North Atlantic (20°S:70°N); (left) model;

(right) Globcolour data.





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Figure 8: Monthly chlorophyll bias at sea surface (Log10(model) minus Log10(observations)) for year 2013 (mg.m-3).

IV.1.2 Nitrates

Figure 9 shows a comparison at the sea surface of the nitrate concentration derived from the climatological World Ocean Atlas 2009 and predicted by the model for year 2013. Globally, there is a





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good agreement between the model and the climatology except in the tropical band where nitrate concentrations are too high. The equatorial upwelling seems to be too strong in the model. Due to the trade winds dragging surface water westward, deep nutrient-rich water is upwelled in the eastern part of the basins. Equatorial divergence of currents then spreads these nutrient rich waters westward along the equator and north- and southward from the equator (Coriolis effect). This is the reason of the arrowhead shape of the chlorophyll and nitrate tongue in the Pacific Ocean (Figure 2 and Figure 9 right). In the Pacific Ocean, the nitrate tongue is clearly too strong and stretched (and as a result, the chlorophyll one as well). In the Atlantic and Indian Oceans, nitrate is entirely consumed by phytoplankton whereas in the model, it becomes non-limiting. This discrepancy between model and data is partly due to the assimilation of physical in situ, altimetric data and their associated Mean Dynamic Topography, which creates anomalous vertical velocities. This too strong upwelling has been improved since the last version (see Figure 35 ) but it remains work in progress.

Figure 10 and Figure 11 show respectively equatorial and Pacific sections of the nitrate concentration in the model compared to the WOA 2009 climatology. Here again main structures such as the subtropical gyres are well reproduced by model. Nonetheless, the stronger equatorial upwelling can be seen near the surface, on the Pacific section (Figure 11) and on the equatorial section (Figure 10). It has to be noticed that there is an important variability of the equatorial upwelling (mainly driven by the El Nino Southern Oscillation).

Figure 9: Annual means of nitrate concentration at sea surface (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.





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Figure 10: Annual section of nitrate concentration at the equator (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009

Figure 11: Annual section of nitrate concentration at 155°W in the Pacific Ocean (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

IV.1.3 Phosphates

Figure 12, Figure 13 and Figure 14 show respectively phosphate distribution in 2013 at the sea surface, at the equator and at 155° in the Pacific Ocean, compared with WOA 2009. The same conclusions as in the previous nitrate section can be drawn from these figures. The stronger and westward equatorial upwelling can be seen in Figure 12 as well as near the surface in the Pacific section (Figure 14), and between 130°E and 150°E in the equatorial section (Figure 13). We can notice moreover that phosphate concentration in the oligotrophic Pacific gyre is slightly too high.

Figure 12: Annual means of phosphate concentration at sea surface (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.





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Figure 13: Annual section of phosphate concentration at the equator (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

Figure 14: Annual section of phosphate concentration at 155°W in the Pacific Ocean (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

IV.1.4 Silicate

Idem to nitrates or phosphates (see above).





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Figure 15: Annual means of silicate concentration at sea surface (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

Figure 16: Annual section of silicate concentration at the equator (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

Figure 17: Annual section of silicate concentration at 155°W in the Pacific Ocean (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

IV.1.5 Dissolved Oxygen

For the oxygen variable, the maps at the sea surface are very close to the climatology (see Figure 18). This is expected since sea surface temperature is assimilated in the physical model and temperature strongly constrains the solubility of atmospheric oxygen at the sea surface. Hence, oxygen is also controlled via the assimilation process.

Sections of oxygen concentration are presented on Figure 19 and Figure 20, respectively at the Equator and in the Pacific Ocean. They show a good adequacy between model and climatology (annual mean).





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Figure 18: Annual means of dissolved oxygen concentration at sea surface (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

Figure 19: Annual section of dissolved oxygen concentration at the equator (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.

Figure 20: Annual section of dissolved oxygen concentration at 155°W in the Pacific Ocean (mmol.m-3); (left) model in year 2013; (right) World Ocean Atlas 2009.





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IV.1.6 Iron

Figure 22 shows annual mean of dissolved iron concentration at sea surface for year 2013 predicted by model.

Figure 21: Annual mean of dissolved iron concentration at sea surface (nmol.L-1) for the model in year 2013.

IV.1.7 Phytoplankton biomass in carbon

The annual mean phytoplankton biomass at sea surface predicted by the model is shown in Figure 22.

Figure 23 shows sections along the equator and in the Pacific Ocean, of the mean phytoplankton biomass from model. For the time being, there is not enough data to compare with.

Figure 22 : Annual mean of phytoplankton biomass in carbon at sea surface (mmol.m-3) for the model in year 2013.





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Figure 23 : Section of phytoplankton biomass (mmol.m-3) at the equator (left) and in the Pacific Ocean at 155°W (right).

IV.1.8 Primary production

Figure 24 shows annual mean primary production vertically integrated for the year 2013, from the model (left) and derived from MODIS satellite data (based on the standard VGPM algorithm, right). The same characteristics seen for nutrients above are met here.

The subtropical gyres are too oligotrophic, so there is not enough primary production in these areas (see Figure 24).

In the tropical band, there are too much nutrients, resulting in excessive production, particularly in the eastern part of Equatorial Pacific.

Eastern Boundary Upwelling Systems are not productive enough.

The model underestimates the primary production in the Northern High latitude region.

These comparisons have to be taken with caution because there currently are several algorithms (Standard VGPM (Behrenfeld and Falkowski, 1997), Eppley-VGPM, CbPM etc., see website http://www.science.oregonstate.edu/ocean.productivity/ for more information) allowing to deduce integrated primary production from satellite ocean colour, but they give distinctly different results, and especially in the tropical band.

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





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Figure 24 : Annual means of vertically integrated primary production (gC m-2 day-1) for the year 2013; (left) model; (right) Modis satellite product based on VGPM algorithm (Behrenfeld and

Falkowski, 1997).

Figure 25 : Section of primary production (gC m-3 day-1) at the equator (left) and in the Pacific Ocean at 155°W (right).

IV.2 Forecast assessment

To justify our choice to disseminate forecasts of our biogeochemical model, we compare forecast with persistence. We have 16 weeks of forecasts and associated 8-days Globcolour ocean colour observations from July 2014 to October 2014. We compute the RMS Error between weekly model fields and 8-days observations for respectively the forecast (Figure 26, left) and the persistence fields (Figure 26, right). We can see that the forecasts are relevant (give results closer to observations than persistence) in the tropical band and in the subtropical gyres.





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Figure 26: RMSE of chlorophyll concentration computed between weekly model fields and 8-days ocean colour observations (Globcolour product): (left) forecast; (right) persistence.

To qualify more quantitatively the relevance of forecast, we plot the skill score (Figure 27) of forecast compared with persistence:

Skill Score = 1 – (<(forecast – obs)2>/<(persistence – obs)2)

Skill Score > 0 => the forecast is relevant.

Skill Score < 0 => the forecast is not relevant.

We see that the forecast is relevant in most part of global ocean except in the Gulf Stream region and in the northern part of the Antarctic Circumpolar Current. This is probably due to the model inability to reproduce the seasonal cycle in these regions (a few weeks of time lag diagnosed in these regions).

Figure 27: Skill score of forecast compared with persistence.

The user has to keep in mind that the differences between hindcast and forecast fields remain very weak compared with the differences with observations. Both the hindcast and forecast estimates still bear large uncertainties with respect to climatologies and observations.





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V QUALITY CHANGES SINCE PREVIOUS VERSION

This section deals with the main differences between the old and new versions of the biogeochemical system operated by Mercator Océan.

Compared to World Ocean Atlas 2009 data, both models show great correlation for Silicate, Phosphate, Nitrate, or dissolved Oxygen (> 0.9, see Figure 28). However, the standard deviations of these nutrients in the new model are slightly closer to the data than those in the old model. Moreover, Chlorophyll concentrations, compared to the Globcolour product, are better in the new model (correlation of 0.49; 0.68 for Log10(Chl)) than in the old model (0.37; 0.59 for Log10(Chl)). Globally, the new model shows better results than the old one.

The chlorophyll concentration is globally lower (except at high latitudes) in the new system, which matches more closely satellite observations (Figure 2).

The impact of the higher resolution (new: 1/4°; old: 1°) of the new system is clearly visible: coastal maxima are less diffusive and better match observations (e.g. in the Indonesian archipelago).

The North Atlantic seasonal cycle is slightly better reproduced in the new version of the model (see Figure 5): the chlorophyll concentration is lower during spring bloom which better fits with data and the median is slightly better during summer (more chlorophyll during summer).

The subtropical gyres are slightly more oligotrophic in the new model (see Figure 29), and oligotrophic gyres extend deeper in the new model (see Figure 31).

The tropical band is less excessive in chlorophyll concentration in the new model. This is partly due to a best vertical velocity in the physical model (see Figure 35) and to a tuning of the biogeochemical parameters. However, the tropical band has too high concentrations of nutrients compared to the old system.

Eastern Boundary Upwelling Systems do not extend enough offshore in comparison with data and the old version of the system (see Figure 29).

The Indian Ocean of the new system is less productive than the old one (great improvement). However, chlorophyll concentration in the new version is too low in the Arabian Sea, and upwelling along the Somalia and Oman coasts is not reproduced (see Figure 29 and Figure 2).

At high latitudes, the new system is too much productive in the Northern Hemisphere while the old system was not productive enough. In the southern hemisphere (Southern Ocean), the new system misses the local maximas of chlorophyll due to iron fertilization around island systems (inputs from sediments) for example Georgia Islands (Borrione et al, 2013) or the Kerguelen Plateau. However, nutrients concentrations are closer to data in the Antarctic Circumpolar Current for the new model (see Figure 32).





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Figure 28: Taylor diagram of modelled surface concentration of Silicate, Phosphate, Nitrate, dissolved oxygen, chlorophyll, and LOG10(Chlorophyll), for year 2013, compared to WOA 2009 (Si,

PO4, NO3, O2) and GLOCOLOUR product (CHL, LOGCHL). Correlations are computed on monthly fields.

Figure 29: Annual means of surface chlorophyll concentration (mg.m-3) for year 2013; (left) new model; (right) old model.





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Figure 30: Section of chlorophyll concentration (mg.m-3) at the equator for year 2013; (left) new model; (right) old model.

Figure 31: Section of chlorophyll concentration (mg.m-3) in the Pacific Ocean at 155°W, for year 2013; (left) new model; (right) old model.

Figure 32: Annual mean of nitrate concentration at the sea surface (mmol.m-3), for year 2013; (left) new model; (right) old model.





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Figure 33: Annual section of nitrate concentration at the equator (mmol.m-3), for year 2013; (left) new model; (right) old model.

Figure 34: Annual section of nitrate concentration at 155°W in the Pacific Ocean (mmol.m-3), for year 2013; (left) new model; (right) old model.

Figure 35: Equatorial section of vertical velocities for year 2011: annual mean (m/day) ; (left) New version of the physical system; (right) Old version of the physical system.





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VI NEW FORCING FIELDS SINCE OCTOBER 2016

From October 19th 2016 on, the ocean dynamical forcing fields are the physical CMEMS product GLOBAL_ANALYSIS_FORECAST_PHYS_001_024. The biogeochemical system is still forced offline by daily means, but the ocean dynamics is now run at 1/12° and then coarsened to ¼°. From this date, users can download consistent physical and biogeochemical fields from the CMEMS web portal.

To check that there is no degradation of the biogeochemical system with these new forcing fields, we ran it from January 8th 2014, to December 31st 2015. Hereafter, we present some results of this experiment compared to the system forced by previous forcing fields from the Mercator-Ocean system PSY3V3R3.

Figure 36 and Figure 39 show the comparison of annual mean of chlorophyll and nitrates respectively at sea surface in 2015 between system forced by PSY3V3R3 (left) and forced by GLOBAL_ANALYSIS_FORECAST_PHYS_001_024 (right). It shows a good agreement after a two-year run.

At global scale, there is a clear conformity between the two simulations, which supports the choice of an on-the-fly transition to the new biogeochemical system. We also checked the other variables disseminated in the CMEMS framework (not shown).

However, there are a few differences between the two simulations:

The Gulf Stream region is slightly less rich in chlorophyll in the new release. This feature is observed all year long (not shown). Although an over production remains in this region compared to observation, it is weaker than with previous forcing fields (see Figure 36).

The subtropical gyres are slightly richer in chlorophyll (closer to observations). They were too oligotrophic in the previous system (see Figure 36 and Figure 37).

In the Indonesian archipelago, there is a reduction of the chlorophyll concentration. This is less consistent with observations (see Figure 36 and Figure 38).

The equatorial tongue of nitrates is less developed in the new release which is clearly closer to climatology (see Figure 9).

Figure 36 : Annual mean of surface chlorophyll concentration in 2015 (mg.m-3); (left) with previous forcing fields; (right) with new forcing fields.





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Figure 37 : Annual section of chlorophyll concentration at 155°W in the Pacific Ocean in 2015 (mg.m-3); (left) with previous forcing fields; (right) with new forcing fields.

Figure 38 : Annual section of chlorophyll concentration at the equator in 2015 (mg.m-3); (left) with previous forcing fields; (right) with new forcing fields.

Figure 39 : Annual mean of surface nitrate concentration in 2015 (mmol.m-3); (left) with previous forcing fields; (right) with new forcing fields.

There are a few hypotheses to explain these changes but it is difficult to disentangle where and to what extent each process acts:





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- the physical system is run at a finer spatial resolution. Even if the physical fields are degraded to a coarser resolution (1/4°), the biogeochemical system all the same benefits from the finer resolution physics (Levy et al, 2012).

- a corrected Mean Dynamic Topography whose aim is to diminish some inconsistencies between SSH assimilation on the one hand and T&S assimilation on the other hand.

- the viscosity coefficient was increased : this can reduce the turbulence.

- the assimilation of sea-ice.

- a better control of the observation errors.

- the bathymetry was improved in Indonesian Archipelago.

For more details, please refer to QuID and PUM documents of product GLOBAL_ANALYSIS_FORECAST_PHYS_001_024.

Figure 40 shows the temporal evolution of surface chlorophyll, nitrate, and phytoplankton biomass during the two-year period for the twin experiments. In grey part weekly means are used, in white part monthly means are used. The dashed line shows the start date of the twin experiment.





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On-the-fly change of the forcing fields could create some bumps. Here we see that this is not the case and there is no discontinuity between both simulations in the first weeks. Then we see that both simulations keep the same seasonal cycle. However, with the new forcing the global mean value of chlorophyll, nitrate and phytoplankton concentration is weaker than with the previous one. In the case of nitrates, it must be due to the less extended tropical tongue. It allows to improve the global mean value and to get closer to the climatological mean value (see Table 3). In the case of chlorophyll, it is difficult to compare to the Globcolour global mean value of Table 3 because Globcolour fields contain a lot of masked value in winter at high latitudes where the main differences in this release are found.

Figure 40 : Globally averaged surface chlorophyll (green), nitrate (blue), and phytoplankton biomass in carbon (red) as a function of time.





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VII REFERENCES

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