CMEMS-OC-QUID-009-064-065-093 Global Reprocessed Observation Date : 10/09/2020 Issue : 2.1

Global Reprocessed Observation Product

For the Atlantic and Arctic

Observation Products OCEANCOLOUR_GLO_OPTICS_L3_REP_OBSERVATIONS_009_064 OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065 OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_093

Issue: 2.1

Contributors: S. Pardo, T. Jackson, B. Taylor, J. Netting, B. Calton, B. Howey

Approval Date by Quality Assurance Review Group : 05/01/2021

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CHANGE RECORD

Issue Date § Description of Change Author Validated By

1.0 01/05/2015 all First version of document M. Taberner, S L. Crosnier Pardo, S. Groom, R. Brewin, T. Jackson

1.1 26/01/2016 all ARV2 version R. Brewin, S. Pardo, T. Jackson, M. Grant, B.Taylor

1.2 04/94/2916 all Changes for V2 ARR R. Brewin, S. Pardo, T. Jackson, M. Grant, B.Taylor

1.3 18/01/2016 all Changes for V3 S. Pardo, M. Grant, T. Jackson

1.4 24/03/2017 all V3 answer to review S. Pardo

1.5 21/11/2017 all Changes for V3.3 B. Taylor

1.6 25/01/2018 all Changes for V4 B. Taylor

1.7 23/05/2018 all July Service Release B. Taylor

2.0 04/09/2019 all December 2019 Service Release S. Pardo, J. Netting, Mercator Ocean B. Calton

2.1 10/09/2020 all December 2020 Service Release S. Pardo, J. Netting, Vittorio Brando, B. Calton, B. Howey Shubha Sathyendranath

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TABLE OF CONTENTS

EXECUTIVE SUMMARY ...... 4 Products covered by this document ...... 4 Summary of the results ...... 5 Estimated Accuracy Numbers ...... 6

PRODUCTION SUBSYSTEM DESCRIPTION ...... 7 Production Centre: Ocean Colour Thematic Assembly Centre ...... 7 Production subsystem: PU: OC-PML-PLYMOUTH-UK ...... 7 The ESA Ocean Colour Climate Change Initiative (OC-CCI) Processor at PML ...... 7 The PFT Processor at PML ...... 10

VALIDATION FRAMEWORK ...... 11 Introduction ...... 11 Off-line (in situ) Validation ...... 11

VALIDATION RESULTS ...... 14

SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES ...... 22

QUALITY CHANGES SINCE PREVIOUS VERSION ...... 23

REFERENCES ...... 24

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EXECUTIVE SUMMARY

Products covered by this document This document covers the CMEMS global, reprocessed, observation dataset (GLO REP). The input dataset for the production of GLO REP was made available to CMEMS by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI) project. The OC-CCI project developed and implemented a method of band-shifting, bias-correcting and merging data from multiple ocean colour sensors: SeaWiFS (GAC and LAC), MODIS Aqua, MERIS and VIIRS (OC- CCI 2014a), and in the latest version of CCI, OLCI-3A. The performance of the available atmospheric correction and in-water algorithms was assessed in an open process and the best performing algorithms were selected according to published criteria (OC-CCI 2014b, OC-CCI 2014c). The initial OC-CCI system was developed as described in OC-CCI 2012, and has been updated in every subsequent version. The resulting global products were validated by OC-CCI; compared to the time series of individual sensor data; and compared to the GlobColour dataset (OC-CCI 2014d). A summary of OC-CCI products is provided in Sathyendranath et al. (2019). The CMEMS REP products described in this document have been generated from the most recent OC-CCI v5 dataset, which incorporates the latest NASA reprocessing (NASA R2018.0) for MODIS, VIIRS and SeaWiFS data. The input dataset for the global product is provided at a nominal 4 km resolution on a rectangular grid (0.0416o/pixel) using the OC-CCI processing chain. All input products were daily composites created by accumulating all data from a given sensor for a particular day, band-shifting and bias-correcting SeaWiFS, MODIS- Aqua, VIIRS and OLCI-3A bands to the MERIS bands, and 'merging' all data available for that day (whether from SeaWiFS, VIIRS, MODIS-Aqua, OLCI-3A and/or MERIS). The OCEANCOLOUR_GLO_OPTICS_L3_REP_OBSERVATIONS_009_064 product is composed of a subset of remote sensing reflectances (bands 412, 443, 490, 510, 560 and 665) that are directly extracted from those input daily composites and repackaged for compliance with the CMEMS format standards. The OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065 product contains the chlorophyll concentration variable, directly extracted from the input OC-CCI daily composites and repackaged for compliance with the CMEMS format standards. The chlorophyll algorithm used in OC-CCI is a blend of OC2, OCx, OCI and OCI2, applied to the reflectances, with the blend proportion dependent on the membership of various water types in the pixel in question (Jackson et al., 2017). The algorithms used are chosen on the basis of extensive round-robin exercises (OC-CCI 2014c; Brewin et al. 2015a, Müller et al. 2015a,b) that considered both quantitative analyses against in situ data and qualitative considerations obtained from user consultations, such as meeting GCOS requirements for error characteristics (OC-CCI 2014c). The criteria were developed to meet the requirements for climate quality data (Sathyendranath et al. 2017). From December 2019 on, the OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065 product also contains the CMEMS phytoplankton functional type (PFT) variables expressed as chlorophyll concentration in sea water contained in three size classes: Micro (Micro-phytoplankton), Nano (Nano-phytoplankton) and Pico (Pico- phytoplankton). Note that, in the global products, the functional types are grouped according to size (Vidussi et al., 2001), with microplankton representing phytoplankton types with nominal sizes greater than 20 μm (for example, diatoms and dinoflagellates), nano-phytoplankton with sizes in the 2-20 μm range (for example, nanoflagellates and cryptophytes), and the picophytoplankton grouping small cells of less than 2 μm in nominal size (for example, Synechococcus and Prochlorococcus). Hence it would be also appropriate to refer to these classes as “Phytoplankton Size Class” (PSC) variables. The PFT products are made available at 4 km resolution following Brewin et al. (2015b). The OC-CCI Rrs have also been provided at the CMEMS regional level (1 km) and the regional algorithms applied to retrieve chlorophyll concentrations. Quality information for the regional algorithms is available in the corresponding regional QUID (CMEMS-OC-QUID-009-066-067-068-069-088-091). The OC-CCI project came to an end in 2018. Production of ECVs (essential climate variables) based on the OC-CCI method will be continued as part of the Copernicus Climate Change Service (C3S), while R&D activities will carry

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on within the framework of ESA’s CCI+. This means that documentation and products will be added and will evolve over time. Differing timelines between CMEMS and OC-CCI/C3S/CCI+ mean that new or updated documentation might be made available by ESA/Copernicus, which supersedes or augments the information presented here. The user is, therefore, referred to the OC-CCI (www.esa-oceancolour-cci.org), C3S and CCI+ websites for more detailed information and documentation. The development of the PFT variables at the regional scale (for the Atlantic) is based on the results of the TOSCA (Towards Operational Size-class Chlorophyll Assimilation) project funded through the CMEMS Service Evolution 21-SE-CALL1 (Ciavatta et al., 2018), whereas the global products described here are based on Brewin et al. (2015b), because the algorithm described in Brewin et al. (2015b) has been validated at the global scale, whereas the TOSCA products were tuned only for the Atlantic. Hence, some differences are anticipated between the PFT variables in the Atlantic regional products and the global products. Note further that, for want of global validation for the method of partitioning micro-phytoplankton into dinoflagellates and diatoms, this breakdown is not provided for the global products, only for the Atlantic regional products where they have been validated. Table 1 shows the list of CMEMS products and their main characteristics, for which the scientific validation is provided in this document.

EPST name (OC-PML-PLYMOUTH-UK)

OCEANCOLOUR_GLO_CHL_L3_REP_OBSERVATIONS_009_065 OCEANCOLOUR_GLO_OPTICS_L3_REP_OBSERVATIONS_009_064 OCEANCOLOUR_GLO_CHL_L4_REP_OBSERVATIONS_009_093

EPST number (OC-PML-PLYMOUTH-UK)

Validatio n Available Variable Product Period Zone Resolutio n 009_065 GLO 4 km CHL daily Archived Off-line 009_065 GLO 4 km PFT daily Archived Off-line 009_064 GLO 4 km Optical daily Archived Off-line 009_093 GLO 4 km CHL monthly Archived Off-line Table 1: List of products for which the scientific validation information is provided in this document.

OC-CCI Global Variables delivered by OC-PML-PLYMOUTH-UK

CHL Chlorophyll concentration

PFT Phytoplankton functional types – nano-, pico- and micro- phytoplankton. Note that these types may also be referred to as phytoplankton size classes (PSC)

Rrs Remote sensing reflectances

Table 2: List of OC-CCI global, reprocessed, variables furnished by OC-PML-PLYMOUTH-UK.

Summary of the results One of the main advantages attributed to the OC-CCI dataset, but not covered in this document, is the considerable improvement in the number of retrievals from the merged product when compared to the single

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sensor products (OC-CCI 2014d) largely on account of use of multiple sensors, as well as a “state of the art” atmospheric correction (Polymer: Steinmetz et al., 2011) applied to all sensors but SeaWiFS. Chlorophyll The CHL results for the global in situ product show a strong correlation (r2 0.878) with low error (Ψ 0.23 and D 0.23) and low bias (δ -0.005) for some 37709 matched observations. Remote Sensing Reflectances The OC-CCI Rrs also have strong correlations with the highest value at 490nm (r2 0.855) and with very low errors and biases (highest values are Ψ 0.0001, δ 0.0).

Estimated Accuracy Numbers Table 3 shows the available Estimated Accuracy Numbers available from the in situ validation exercises. These results are described in greater detail in section 0. The inclusion of OLCI-3A in CCIv5, combined with an extension of the in situ database and time series has led to an increase in the number of RRS matchups.

Variable N r2 Slope Intercept Bias RMSD

CHL vs. combined in situ 37427 0.878 0.902 -0.056 -0.005 0.230

Rrs412 vs. combined in situ 28188 0.836 0.910 0.001 -0.001 0.001

Rrs443 vs. combined in situ 33545 0.833 0.888 0.001 -0.001 0.001

Rrs490 vs. combined in situ 33202 0.855 0.829 0.001 0.0 0.001

Rrs510 vs. combined in situ 14428 0.275 0.621 0.001 0.0 0.001

Rrs560 vs. combined in situ 13150 0.801 0.776 0.0 0.0 0.0

Rrs665 vs. combined in situ 10666 0.664 0.625 0.0 0.0 0.0

PFT Micro vs combined in 729 0.740 0.789 -0.194 0.027 0.441 situ

PFT Nano and Pico vs 1458 0.448 0.580 -0.310 -0.100 0.379 combined in situ

PFT Nano vs combined in situ 729 0.549 0.706 -0.102 -0.168 0.407

PFT Pico vs combined in situ 729 0.238 0.243 -0.745 -0.036 0.337

Table 3: Estimated accuracy numbers of the validation of the GLO REP product.

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PRODUCTION SUBSYSTEM DESCRIPTION

Production Centre: Ocean Colour Thematic Assembly Centre The OCTAC is a distributed processing centre made of three independent Production Units (PU). The main objective of OCTAC is to build and operate a European Ocean Colour Service for Copernicus marine applications providing global, pan-European and regional (Atlantic, Arctic, Baltic, Mediterranean, and Black Seas) high-quality ocean colour products. PML is a production unit within the CMEMS Ocean Colour Thematic Assembly Centre (OCTAC).

Production subsystem: PU: OC-PML-PLYMOUTH-UK The products produced by the OCTAC are the spectral normalized remote sensing reflectance (Rrs), the concentration of chlorophyll (CHL) and the phytoplankton functional types (PFTs). The OCTAC focus is primarily on the operational provision of data; however, in combination with the European Space Agency Ocean Colour Climate Change Initiative (OC-CCI) project and its successor CCI+, a climate quality, historical, reprocessed dataset has been made available to the CMEMS community. PML as project lead in the OC-CCI and CCI+ projects, and a Production Unit of the OCTAC, has taken responsibility for providing the OC-CCI data to CMEMS. This involves importing, repackaging, and documenting OC-CCI products within CMEMS. This document describes the main achievements of the validation activity performed over the ocean colour CCI GLO REP products in the CMEMS context.

The ESA Ocean Colour Climate Change Initiative (OC-CCI) Processor at PML This section briefly recapitulates an overview of the processing chain, schematically presented in Figure 1. A thorough description of the OC-CCI processing chain is given in OC-CCI (2014e). Input datasets The input EO datasets were • MERIS level 1b 3rd reprocessing. • MODIS level 1 R2018.0 • SeaWiFS level 2 GAC and LAC (4 km) R2018.0 • VIIRS level 1 R2018.0 • OLCI-3A

Binning and band shifting All sensors were binned to level 3 with the BEAM binner to ensure consistency. MODIS, SeaWiFs, VIIRS and OLCI- 3A were band shifted to the six main MERIS bands (412, 443, 490, 510, 560, 665 nm) by computing Inherent Optical Properties using the Quasi-Analytical Algorithm QAA of Lee et al. (2002) and back computing the Rrs values at the required MERIS bands using a high-resolution spectral model consistent with the QAA algorithm. The output Rrs for 412-560 nm were cleaned of any negative values, with the data items removed. Negative Rrs values in the 665 nm band frequently occur due to low signal levels, and these were clamped to zero. There was no band shifting of the MERIS data.

Bias correction The band shifted SeaWiFS, MODIS, VIIRS and OLCI-3A Rrs were corrected to remove mean differences (biases) against MERIS Rrs. The correction was done on a per-pixel basis using biases computed from ±45

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days of temporally-weighted daily climatologies from 2003-2007, derived from weekly composites, with limited spatial interpolation applied to reduce missing data. This pre-compositing approach for the input days reduces outliers in the process and significantly increases bias map coverage (which in turn increases final product coverage).

Product generation A range of products can be computed from the merged Rrs, either directly using the algorithms in SeaDAS or using independently implemented algorithms. Algorithms were selected from the best performers in the round- robin evaluation: • Chlorophyll: a blend of OC2, OCx, OCI and OCI2, depending on the water types represented in each pixel (see Jackson et al. 2017 for further details on the optical-class-based method of implementing chlorophyll algorithms) • Rrs: Polymer for all sensors except SeaWiFS.

Uncertainty estimation The user consultation that was undertaken at the beginning of the OC-CCI project revealed that the user community required uncertainty estimates that are based on validation of the products against matched in situ observations. The uncertainty metrics of Root-Mean-Square Uncertainty (RMSU) and bias are computed from match-up datasets. Optical classification of the pixels provided the basis of extrapolating errors to each pixel (Jackson et al. 2017). Note that these estimates are only as good as the quality and representativeness of the in situ match up data sets that were available for uncertainty estimation. Geographical coverage and representation of different water types in the in situ data were better for chlorophyll than other products as it is more commonly measured in situ. Per-pixel uncertainty estimates were computed following Moore et al. (2009) and Jackson et al. (2017). In short, the membership of every pixel in 15 water classes is computed, then a pixel-specific total uncertainty is computed using these memberships and a table of uncertainties per class (Sathyendranath et al. 2019). The uncertainty tables were computed from matchups between the CCI v4.2 data and a published, freely available in situ database (Valente et al., 2019). N.B. per request, the uncertainty fields have not been included in the CMEMS distribution but can be obtained from the original dataset released by the OC-CCI project to which the interested reader is referred (www.oceancolour.org).

Reprojection and further processing All data were re-projected onto a geographic grid, metadata added, product subsets created, and PNG quicklooks created.

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Figure 1: Processing chain of OC-CCI V5 (as inherited into CMEMS)

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The PFT Processor at PML This section briefly recapitulates an overview of the processing chain. Method An abundance-based method (Brewin et al., 2010) was re-tuned to estimate the chlorophyll concentration of three phytoplankton groups, partitioned according to size, from satellite data in the global oceans (Brewin et al. 2015b). Data collected in sixteen in situ sampling campaigns (see Figure ) was used to re-tune, adapt and validate the model of Brewin et al. (2010), compute the root mean square error (Ψ) and bias (δ) map phytoplankton functional type products and associated errors using ocean-colour data. Samples were also matched to daily, level 3 (4 km sinusoidal projected) satellite chlorophyll data, from version 1.0 of the Ocean Colour Climate Change Initiative (OC-CCI, a merged MERIS, MODIS-Aqua and SeaWiFS product available at http://www.oceancolour.org/), between 1997 and 2012. Each in situ sample was matched in time (daily temporal match-up) and space (latitude and longitude) with the satellite data. Validation using the latest version of the Ocean Colour Climate Change Initiative, currently v5.0, was completed using the original datasets in the TOSCA project (Brewin et al. 2015).

Input datasets: chlorophyll The input dataset for the production of GLO REP PFTs was made available to CMEMS by the ESA Ocean Colour Climate Change Initiative (ESA OC-CCI) project. A description of this dataset is provided in previous sections.

Processing stages The regional size-class algorithms developed in Brewin et al. (2015) have been successfully operationalised at PML and are running as part of routine data processing using the latest version of CCI. The system has been designed to run as a Python module within PML’s Generic Earth Observation Processing System (GEOPS), which runs a series of processing stages defined by an XML configuration file.

The implementation starts with level 2 chlorophyll data and uses the chlorophyll-based size-class algorithm as defined in Brewin et al. (2015b). But note that the light dependence in the parameters was not implemented, primarily because the original paper did not show that it reduced the uncertainties significantly. The implementation allows for easy integration of different model parameter sets, so that it can be easily adjusted with parameter sets tuned for different geographical regions. The initial implementation contains a parameter set suitable for global oceans.

Outputs from the PFT processing stage are generated as flat binary data files, which are then integrated by the GEOPS processing chain into a netCDF output file that contains all of the products generated during the processing run. Unit tests have been written to enable automated testing of code updates.

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VALIDATION FRAMEWORK

Introduction For the operational chain the OCTAC provides ‘off-line’ validation, usually from a comparison between in situ and satellite data, carried out once, and an ‘on-line’ quality check, mainly based on climatologies that are used to determine whether regions or individual pixels are suspect. For the OC-CCI global reprocessed dataset, the ‘on- line’ mode is not appropriate so the ‘off-line’ mode is presented herein. Further quality information can be found in the OC-CCI documentation. Off-line (in situ) Validation The validation described in this document was performed off-line; common metrics were adopted for all products when possible (see section below). For all products, match-ups were made with in situ field measurements collected by a number of campaigns (summarized in Table 4). These datasets contain ocean, shelf and coastal waters, and employ different measurement methods: for example, chlorophyll measurements are obtained using high performance liquid chromatography (HPLC), fluorometry and particulate absorption (Acs).

The multi-year data were delivered in daily-composite form, incorporating more than one sensor (e.g. SeaWiFS, MERIS, MODIS, VIIRS, OLCI-3A); therefore, match-ups were selected within a ±12 hours interval (see Brewin et al., 2015a). Satellite values were extracted for the 3x3 pixel element centred at the in situ point. Following standard methods (Bailey & Werdell, 2006) the coefficient of variation (CV, median coefficient of variation for Rrs bands between 412 and 560 nm) was computed for each 3x3 pixel box. To ensure homogeneity and good quality, match-ups were only included if the coefficient of variation over the box was CV < 0.15, and if more than 50% of the pixels in the box were valid. The median of the nine pixels was considered as the satellite estimate for further analysis. Finally, the performance of the dataset was assessed using the metrics described in the following section.

Paramet Name Provider Description ers

Atlantic Meridional 1997-2018, UK to Falkland Islands, variable transect, AMT Chl-a Transect distributed by the British Oceanographic Data Centre.

OC Climate Change 1997-2018 (v7), global coverage and wide value range, OC-CCI-insituv7 Chl-a, Rrs Initiative distributed by OC-CCI.

2003-2006, Regional Validation of MERIS chlorophyll REVAMP PML Chl-a Products in North Sea Coastal Waters, PML internal distribution.

1997-2006, NW Atlantic, distributed as part of the BioChem Bedford Institute of database by Fisheries and Oceans Canada (DFO) BEDFORD Chl-a Oceanography https://inter-j01.dfo-mpo.gc.ca/urmb-pgeu/start- debut?app=biochemQuery&lang=eng

Table 4: in situ datasets

Statistical tests for the validation The quantitative statistical tests that were used in the in-water comparison are described below.

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Determination coefficient (r2) The determination coefficient r2 (also called squared Pearson’s product moment correlation) is often used in model comparison, calculated according to:

N 2 ⎡ E M ⎤ X E − X X M − X ⎢∑( i )( i )⎥ 2 ⎣ i=1 ⎦ r = N N E 2 M 2 X E − X X M − X ∑( j ) ∑( k ) j=1 k=1 where, X is the variable and N is the number of samples. The superscript E denotes the estimated variable (from the model) and the superscript M denotes the measured variable. The bar over a variable represents the mean. Note that the determination coefficient assumes a linear relationship between variables and normal distributions for each of the variables. The correlation coefficient may take any value between 0 and +1.0. It provides a measure of the variance in the measured variable that is explained by the estimated variable (from the model). The determination coefficient is computed in log10 space for chlorophyll and linear space for optics, considering chlorophyll is approximately is log-normally distributed.

The Root Mean Square Uncertainty, RMSU (Ψ) The performance of the matchups can be quantified using the Root Mean Square Difference (Ψ) between the satellite and in situ variables. Considering that chlorophyll-a concentration spans over four orders of magnitude, and are approximately log-normally distributed, chlorophyll concentrations are log-transformed prior to calculation of RMSU. No log transformation was carried out for the other variables in the product suite. RMSU is calculated as:

where, X is the variable (in log10 space in the case of chlorophyll concentrations) and N is the number of samples. The superscript E denotes the estimated variable (from the satellite) and the superscript M denotes the measured variable (from in situ).

The bias (δ) The error between estimated and measured variables can be partitioned into the bias and variance components. The bias can be expressed according to

where, X is the variable (in log10 space) and N is the number of samples. The superscript E denotes the estimated variable (from satellite) and the superscript M denotes the measured variable (in situ).

The centred-pattern (or unbiased) root mean square difference, RMSD (Δ) The unbiased Root Mean Square Difference, denoted Δ, can be expressed according to

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Type 2 regression (slope and intercept) Linear regression presumes that the measured variable (in situ data) is known infinitely well. However, as with the satellite measurements, the measured data also has uncertainties. Unfortunately, the exact errors in the in situ data are unknown. However, when both variables being compared have error, Type 2 regression can be performed. As with linear regression, the estimated variable is first regressed on the measured variable such that

where the retrieved parameters I1 and S1 represent the intercept and the slope, respectively for the fit. Then, the measured variable is regressed on the estimated variable such that

where the retrieved parameters I2 and S2 represent the intercept and the slope, respectively for the second fit. A new slope (S) is then derived according to

Having derived the new slope (S), the new intercept (I) can be calculated according to

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VALIDATION RESULTS

Chlorophyll (CHL) results Table 5 summarises the results of the statistical tests for the chlorophyll GLO REP product versus the global in situ datasets with the higher number of match-ups, including a combined dataset. Figure 2 shows the scatterplot for the GLO REP chlorophyll versus the combined dataset. Note that the chlorophyll analysis was performed on log10 transformed data. The OC-CCI products are designed to be stable, error characterised and to strive towards meeting the ECV climate quality criteria. The high quality of the product is seen in the extremely good r2, low RMSD and very low bias. It can be seen that in terms of bias, the OC CCI chl-a product is within the GCOS Target Requirement of 5% accuracy, suggesting that the OC CCI programme has met the GCOS target.

N S I r2 Ψ δ Δ

AMT-19 4322 1.158 0.140 0.938 0.113 0.025 0.11

AMT-22 1227 0.278 -0.960 0.147 0.160 0.077 0.140

AMT-23 1223 1.179 0.271 0.746 0.150 -0.072 0.132

AMT-24 394 1.126 0.368 0.466 0.278 -0.248 0.126 AMT-26 4594 0.684 -0.295 0.563 0.132 -0.040 0.125

AMT-27 2610 1.017 0.100 0.934 0.128 -0.082 0.098

AMT-28 5621 0.967 -0.072 0.954 0.115 0.043 0.107 OC-CCI-insituv7 4786 0.824 -0.025 0.82 0.294 -0.017 0.294 (HPLC) OC-CCI-insituv7 11932 0.764 -0.032 0.728 0.310 0.001 0.310 (FLUOR) REVAMP 76 0.688 0.136 0.705 0.279 0.0279 0.279

BEDFORD 642 0.644 -0.105 0.639 0.355 0.041 0.352

All Combined 37427 0.902 -0.056 0.878 0.230 -0.005 0.230

Table 5: GLO CHL validation results

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Figure 2: GLO REP CHL vs in situ

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Remote Sensing Reflectances (RRS): Results Table 6 shows the results of the level 3 CCI remote sensing reflectances (Rrs) validation against the OC-CCI v7 in situ dataset (the only in situ database of those listed in Table 4 that includes in situ reflectances). Figures 3 to 8 show the Rrs data versus the in situ scatterplots. The OC-CCI Rrs results for the GLO REP in situ dataset have strong correlations with the highest value at 490nm (r2 0.855) and with very low errors and biases (highest values are Ψ 0.0001, δ 0.00 ). None of the errors or biases are very much higher than this. Although the number of match-ups for all RRS is not as high as for chlorophyll (highest N 33545), nevertheless there are a good number of match-ups on which to base the statistical analysis.

GLO N S I r2 Ψ δ Δ Rrs412 vs OC-CCI- 28188 0.910 0.001 0.836 0.002 -0.001 0.001 insituv7 Rrs443 vs OC-CCI- 33545 0.888 0.001 0.833 0.001 -0.001 0.001 insituv7 Rrs490 vs OC-CCI- 33202 0.829 0.001 0.855 0.001 0.0 0.001 insituv7 Rrs510 vs OC-CCI- 14428 0.621 0.001 0.275 0.001 0.0 0.001 insituv7 Rrs560 vs OC-CCI- 13150 0.776 0.0 0.801 0.0 0.0 0.0 insituv7 Rrs665 vs OC-CCI- 10666 0.625 0.0 0.664 0.0 0.0 0.0 insituv7 Table 6: GLO REP Rrs validation results

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Figure 1: GLO REP Rrs412 vs in situ

Figure 2: GLO REP Rrs443 vs in situ

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Figure 3: GLO REP Rrs490 vs in situ

Figure 4: GLO REP Rrs510 vs in situ

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Figure 5: GLO REP Rrs560 vs in situ

Figure 6: GLO REP Rrs665 vs in situ

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Phytoplankton Functional Types (PFT): Results Considering the agreement between total satellite and in situ chlorophyll in the validation dataset, see Figure 10: GLO REP chlorophyll content for each PFT plotted against in-situ measurements , the satellite estimates of PFTs in chlorophyll units show a correlation with the independent in situ data (Table 7, r2 = 0.238 to 0.740 , and Ψ = 0.339 to 0.442), in agreement with previous studies (Brewin et al., 2010, 2012).

N S I r2 Ψ δ Δ

Micro 0.442 729 0.789 -0.194 0.740 0.027 0.441 >20 μm

Nano + Pico 0.392 1458 0.580 -0.310 0.448 -0.100 0.379 <20 μm

Nano 0.441 729 0.706 -0.102 0.549 -0.168 0.407 2-20 μm

Pico 0.339 729 0.243 -0.745 0.238 -0.036 0.337 <2μm

Table 7: PFT validation results for the Brewin et al. (2015b) model.

Figure 9: Locations of in situ data used for validation. The letters denote the source of data and the number in parenthesis denotes the number of samples. Sources of the data are as supplied in full in Brewin et al. (2015)

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Figure 10: GLO REP chlorophyll content for each PFT plotted against in-situ measurements

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SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES

Date Change/Event description System version other

01/09/2016 Release of OC-CCI v3.0 v3

08/05/2017 Release of OC-CCI v3.1. Note that although the official v3 release was on this date, the processing for this was made available for CMEMS for the release of CMEMS v3.0.

15/01/2018 Baseline change R2014 -> R2018 for MODIS, VIIRS

15/01/2019 Release of OC-CCI v4, based on R2018.

02/12/2019 Incorporation of PFT products. v5

04/09/2020 Release of ESA CCI v5.0 for CMEMS which includes OLCI- v5 3A data, retirement of single sensor products from MODIS and VIIRS

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QUALITY CHANGES SINCE PREVIOUS VERSION

Improvements of the December 2020 release • The REP (long timeseries starting in 1997) has been extended until June 2020 • These chlorophyll and Rrs products are based on the latest release of the Ocean Colour CCI dataset(v5). The merged sensor data now include OLCI-3A and bias correction. It uses MERIS as reference, and not SeaWiFS, as was the case in previous versions. Figure 11 shows that this inclusion of OLCI-3A has increased the number of valid pixels, and there is also a slight increase in the number of valid pixels for both SeaWIFS and VIIRS due to a change in the atmospheric processing.

Figure 11: OC-CCI v4 and OC-CCI v5 total number of valid pixels

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REFERENCES

Bailey, S.W., & Werdell, P.J. (2006). A multi-sensor approach for the on-orbit validation of ocean color satellite data products. Remote Sensing Environment, 102, 12–23. http://dx.doi.org/10.1016/j.rse.2006.01.015 Brewin, R. J., Sathyendranath, S., Hirata, T., Lavender, S. J., Barciela, R. M., & Hardman-Mountford, N. J. (2010). A three-component model of phytoplankton size class for the Atlantic Ocean. Ecological Modelling, 221(11), 1472- 1483. Brewin, R. J. W., Hardman-Mountford, N. J., Lavender, S., Raitsos, D. E., Hirata, T., Uitz, J., et al. (2011). An intercomparison of bio-optical techniques for detecting dominant phytoplankton size class from satellite remote sensing. Remote Sens. Environ. 115, 325–339. Brewin, R. J. W., Hirata, T., Hardman-Mountford, N. J., Lavender, S., Sathyendranath, S., and Barlow, R. (2012). The influence of the Indian Ocean Dipole on interannual variations in phytoplankton size structure as revealed by Earth Observation. Deep Sea Res. II 77–80, 117–127. doi: 10.1016/j.dsr2.2012.04.009 Brewin, RJW, Sathyendranath, S, Müller, D, Brockmann, C, Deschamps, P-Y, Devred, E, Doerffer, R, Fomferra, N, Franz, B, Grant, M, Groom, S, Horseman, A, Hu, C, Krasemann, H, Lee, ZP, Maritorena, S, Mélin, F, Peters, M, Platt, T, Regner, P, Smyth, T, Steinmetz, F, Swinton, J Werdell, J, White, GN, III (2015a) The Ocean Colour Climate Change Initiative: III. A round-robin comparison on in-water bio-optical algorithms. Remote Sensing of Environment, 162: 271-294. http://dx.doi.org/10.1016/j.rse.2013.09.016 Brewin, R.J.W., Sathyendranath, S., Jackson, T., Barlow, R., Brotas, V., Airs, R., Lamont, T. (2015b) Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class, Remote Sensing of Environment 168 (2015) 437–450 http://dx.doi.org/10.1016/j.rse.2015.07.004 Brewin, R. J., Dall'Olmo, G., Pardo, S., van Dongen-Vogels, V., Boss, E. S. (2016). Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals, Remote Sensing of Environment, v183, 2016, 82-97. Brewin, R.J., Ciavatta, S., Sathyendranath, S., Jackson, T., Tilstone, G., Curran, K., Airs, R.L., Cummings, D., Brotas,V., Organelli, E. and Dall'Olmo, G., (2017). Uncertainty in Ocean-Color Estimates of Chlorophyll for Phytoplankton Groups. Frontiers in Marine Science, 4, p.104. Ciavatta, S., Brewin, R.J.W., Skakala, J., Taylor, B., TOSCA “Towards Operational Size-class Chlorophyll Assimilation” CMEMS Service Evolution 21-SE-CALL1 final report, 2018. Glover, D.M., W.J. Jenkins, and S.C. Doney, 2011: Modeling Methods for Marine Science, Cambridge University Press, Cambridge, UK, 592 pp. Jackson, T, Sathyendranath, S, Mélin, F (2017) An improved optical classification scheme applied to ocean colour ECV. Remote Sensing of Environment. 203: 152–161. http://dx.doi.org/10.1016/j.rse.2017.03.036 Lee, Z. P., Carder, K. L. and Arnone, R. A. (2002) Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. Appl. Opt., 41, 5755-5772. Moore, T.S, Campbell, J.W., Dowell, M.D. (2009) A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product, Remote Sensing of Environment 113. 2424–2430. Müller, D, Krasemann, H, Brewin, RJW, Brockmann, C, Deschamps, P-Y, Doerffer, R, Fomferra, N, Franz, B, Grant, M, Groom, S, Mélin, F, Platt, T, Regner, P, Sathyendranath, S, Steinmetz, F, Swinton, J (2015a) The Ocean Colour Climate Change Initiative: I. A Methodology for Assessing Atmospheric Correction Processors based on In-situ Measurements. Remote Sensing of Environment, 162: 242-256. http://dx.doi.org/10.1016/j.rse.2013.11.026 Müller, D, Krasemann, H, Brewin, RJW, Brockmann, C, Deschamps, P-Y, Doerffer, R, Fomferra, N, Franz, B, Grant, M, Groom, S, Mélin, F, Platt, T, Regner, P, Sathyendranath, S, Steinmetz, F, Swinton, J (2015b) The Ocean Colour Climate Change Initiative: II. Spatial and Temporal Homogeneity of satellite data retrieval due to systematic effects in Atmospheric Correction Processors. Remote Sens. Env. 162: 257-270. http://dx.doi.org/10.1016/j.rse.2015.01.033

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NASA 2013 http://seabass.gsfc.nasa.gov/seabasscgi/nomad.cgi OC-CCI 2012, OC CCI System Requirements Document, Ref:SRD, 25/01/2012, Issue:1.02, ESA/ESRIN, AO- 1/6207/09/I-LG, http://www.esa-oceancolour-cci.org OC-CCI 2014a, Ocean Colour Data Bias Correction and Merging, Ref: D2.6, 10 January 2014, Issue: 2.1, ESA/ESRIN, AO-1/6207/09/I-LG, http://www.esa-oceancolour-cci.org OC-CCI 2014b, Product Validation and Algorithm Selection Report (PVASR) Part 1 - Atmospheric Correction, D2.5, 2014-01-31, 2.0, ESA / ESRIN, AO-1/6207/09/I-LG, http://www.esa-oceancolour-cci.org OC-CCI 2014c, Product Validation and Algorithm Selection Report (PVASR) Part 2 – In-Water Algorithms, D2.5, 21/01/14, 2.0, ESA / ESRIN, AO-1/6207/09/I-LG, http://www.esa-oceancolour-cci.org OC-CCI 2014d, Product Validation and Inter-comparison Report (PVIR), D4.1, 2014-05-20, 1.0.2, ESA / ESRIN, AO- 1/6207/09/I-LG, http://www.esa-oceancolour-cci.org OC-CCI 2014e, System Prototype Description, Ref: D3.1, 11/02/14, Issue: 1.1, ESA/ESRIN, AO-1/6207/09/I-LG, http://www.esa-oceancolour-cci.org O'Reilly, J.E., et al., 2000: SeaWiFS Postlaunch Calibration and Validation Analyses, Part 3. NASA Tech. Memo. 2000-206892, Vol. 11, S.B. Hooker and E.R. Firestone, Eds., NASA Goddard Space Flight Center, 49 pp. Sathyendranath, S, Brewin, RJW, Brockmann, C, Brotas, V, Calton, B, Chuprin, A, Cipollini, P, Couto, AB, Dingle, J, Doerffer, R, Donlon, C, Dowell, M, Farman, A, Grant, M, Groom, S, Horseman, A, Jackson, T, Krasemann, H, Lavender, S, Martinez-Vicente, V, Mazeran, C, Mélin, F, Moore, TS, Müller, D, Regner, P, Roy, S, Steele, CJ, Steinmetz, F, Swinton, J, Taberner, M, Thompson, A, Valente, A, Zühlke, M, Brando, VE, Feng, H, Feldman, G, Franz, BA, Frouin, R, Gould, Jr., RW, Hooker, SB, Kahru, M, Kratzer, S, Mitchell, BG, Muller-Karger, F, Sosik, HM, Voss, KJ, Werdell, J, and Platt, T (2019) An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors: 19, 4285. doi:10.3390/s19194285 Sathyendranath, S, Brewin, RJW, Jackson, T, Mélin, F, Platt, T (2017) Ocean-colour products for climate-change studies: what are their ideal characteristics? Remote Sensing of Environment. 203: 125–138. http://dx.doi.org/10.1016/j.rse.2017.04.017 Steinmetz F, Pierre-Yves Deschamps, P-Y, Ramon, D (2011) Atmospheric correction in presence of sun glint: application to MERIS, Optics Express, Vol. 19, Issue 10: 9783-9800. http://dx.doi.org/10.1364/OE.19.009783. Valente, A, Sathyendranath, S, Brotas, V, Groom, S, Grant, M, Taberner, M, Antoine, D, Arnone, R, Balch, WM, Barker, K Barlow, R, Bélanger, S, Berthon, J-F, Besiktepe, S, Borsheim, Y, Bracher, A, Brando, V, Canuti, E, Chavez, F, Cianca, A, Claustre, H, Clementson, L, Crout, R, Frouin, R, García-Soto, C, Gibb, SW, Gould, R, Hooker, SB, Kahru, M, Kampel, M, Klein, H, Kratzer, S, Kudela, R, Ledesma, J, Loisel, H, Matrai, P, McKee, D, Mitchell, BG, Moisan, T, Muller-Karger, F, O’Dowd, L, Ondrusek, M, Platt, T, Poulton, AJ, Repecaud, M, Schroeder, T, Smyth, T, Smythe- Wright, D, Sosik, HM, Twardowski, M, Vellucci, V, Voss, K, Werdell, J, Wernand, M, Wright, S, and Zibordi, G (2019) A compilation of global bio-optical in situ data for ocean-colour satellite applications – version two. Earth Syst. Sci. Data, 11, 1037–1068. https://doi.org/10.5194/essd-11-1037-2019 Vidussi, F., Claustre, H., Manca, B.B., Luchetta, A., Marty, J.C., 2001. Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter. Journal of Geophysical Research 106 (C9), 19939–19956. Werdell, P.J., Bailey, S. W., 2005: An improved bio-optical data set for ocean color algorithm development and satellite data product validation. Remote Sensing of Environment , 98(1), 122-140.