Siegel, McClain, Maritorena, Behrenfeld, Bailey, Franz, Bontempi, Antoine, d’Andon, Wielicki, others???
For Remote Sensing of the Environment
Date: 7/24/08 – DV
Observations of the global biosphere are needed to assess the changes of our living oceans, to attribute the causal factors creating these changes and to provide the data sets from which we will develop and validate models to forecast future changes. Here we present an assessment of the challenges facing the creation of climate data records from satellite ocean color observations.
Climate data records are by their very nature a long term data records of key observables. Hence the construction of a satellite ocean color climate data record (OC-CDR) requires the bridging data from sequential satellite missions. Further, OC-CDR’s must be constructed with the goal of answering important questions for changes of the ocean biosphere on decadal time scales. This requires defining relevant metrics for an OC-CDR, including expected trends that the OC-CDR is designed to quantify, the acceptable levels of uncertainty driven by sampling, and the degree to which a gap in the OC-CDR is acceptable. These factors all have an influence on the construction of an OC-CDR from existing data sets and the implementation of future satellite missions meant to contribute to OC-CDR’s. We illustrate these points using existing satellite ocean color data and simple models of the expected sampling across multiple satellite missions.
The goal of this manuscript is to provide an assessment for what needs to be accomplished to meet the challenges of providing true ocean color climate data records for assessing change of our living oceans.

Chuck/Bruce – Wanna take a swipe through the intro? The pieces are there –needs some fleshing out…
Our living oceans are changing due to both natural climate variations as well as forcings directly ascribable to humankind’s actions (e.g., Hjort, 1914; Pauly et al. 1998; Chavez et al., 1998;
Jackson et al. 2001). In order to understand these changes, their impacts and to begin to predict their impacts into the future, we must first document changes in the ocean’s biosphere within the instrumented record. Satellite ocean color observations provide the only way to quantify changes in the global biosphere over multi-decadal time scales (e.g., Yoder et al. 1993; Gregg and Conkright, 2001; 2002; McClain et al. 2001; Antoine et al. 2005; Behrenfeld et al. 2006;
2008; McClain, 2008). Ocean color data products quantify the chlorophyll biomass concentration and other products for a given location and time. This quantification is needed to understand change in our living oceans. This is the purpose of an ocean color climate data record (CDR).
A recent report from the U.S. National Research Council assessed the requirements for creating climate data records from environmental satellite observations (NRC, 2004). This report defines a CDR as “A climate data record is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change” (NRC, 2004). It is clear that CDR’s need to be longer than typical climate time scales of interest and that a CDR constructed from satellite data must be built from multiple missions. However, the operative
(weasel) word in the standard definition of a CDR is “sufficient”. But what defines sufficiency ?
Obviously, the answer to this question is context specific and relates to a specific scientific question. Some climate change questions will be easier to answer than others changing what is meant by sufficiency in the creation of the CDR aimed at answering that specific question.
Defining sufficiency requires a statement of the scientific question of interest and the expected metrics for which we have a expectation that the final CDR will likely answer the question. It is the elucidation of the sufficient length, consistency, and continuity of a CDR for satellite ocean color data observations that is the focus of this contribution.
Previous experiences in the Earth Sciences community – ISCCP, CERES, altimetry, SST, etc.
Talk about why and how these worked (similar sensors across missions, simplish conversions of
TOA observations to geophysical observations, pretty good understanding of the uncertainties in parameter, etc.). See the NRC 2004 report for a list.
Known issues with creating OC CDR’s – small ocean signals from large TOA signals, complex path from TOA obs to ESDR, different sensor characteristics across missions, algorithm differences, gaps in the record, vicarious cal, validation assets, etc. Science issues – rapid turnover of phytoplankton biomass leading to rapid bloom creation and destruction, small scales of process (km to 100 km), cloud biasing (both in sampling and conditioning of the phytoplankton signal), etc. Talk about the missions listed in Table 1 and the differences in sensors designs. Comment about the reinvestment time scales for missions of roughly every 7 years (for gap analysis below).
Here, we present an analysis of some of the major challenges facing the creation of OC-CDR’s with specifically asking how to quantify what is a sufficient length, consistency, and continuity

for a CDR. The logic goes: 1) what are the trends we need to detect to make an OC CDR, 2) what are the probabilities of a gap in the OC data record, 3) how well do we have to measure OC quantities post-gap to achieve our CDR goals, 4) the importance of consistent sampling in constructing global scale CDR’s, 5) what are the realities of creating OC CDR’s from what we have, and 5) provide a set of recommendations of how OC CDR’s should be constructed. Of course this line of thinking can be applied for the construction of other satellite data CDR’s.
For this analysis, global ocean color imagery is used from available level-3 data sets (Table 1).
Global observations from the Sea-viewing Wide Field of view Sensor (SeaWiFS), the MODerate resolution Imaging Spectrometer (MODIS) on the Aqua platform (MODIS-Aqua,), the Medium
Resolution Imaging Spectrometer Instrument (MERIS), the Ocean Color Temperature Scanner
(OCTS) and the Coastal Zone Color Scanner (CZCS). Only missions are included in this study only if extensive use of their global data products has been made in the literature (Table 1).
Chlorophyll data products used are provided from the respective data center for level-3 chlorophyll a concentration. No alterations to the original data sets beyond averaging into particular regions.
Comparisons are made only for chlorophyll a concentration averaged over all deep water regions
(> 1000 m) bounded between 50 o
N and 50 o
(Chl
DW
). The choice of Chl
DW
as the key metric accentuates the need of a CDR to assess small changes in a time series and minimizes the effects of episodic phytoplankton blooms that may not be well sampled by any of the time series
(GlobColour Validation report, 2008). The definition of Chl
DW
eliminates blooms found within coastal regions (shallow waters) or at higher latitudes that are poorly sampled by an ocean color imager. The time series of Chl
DW for all 5 global sampling missions is shown in the left panel of figure 1 and for the SeaWiFS, MERIS and MODIS-Aqua missions in the right panel of figure 1.
Clearly, the CZCS time series of Chl
DW differs from the other 4 missions, which can be attributed to its poor sampling characteristics when aggregated over such large open ocean regions (e.g.,
Gregg and Conkright, 2002; Antoine et al. 2005). The OCTS mission only collected monthly imagery for 8 months and also will not be described in detail below. The role of sampling characteristics will be addressed in greater detail below.
The three other long-term missions, SeaWiFS, MERIS and MODIS-Aqua, are all still collecting imagery (as of May 2008) and have been sampling simultaneously. The Chl
DW
from these missions all show a weak seasonal cycle (0.01 to 0.02 mg m
-3
) as there is XX% more deep water ocean in the southern hemisphere than the northern hemisphere. The SeaWiFS record is the longest and shows clearly the recovery from the 1997/1998 El Nino event (nearly 0.04 mg m
-3
in one year) and then the slower return to a more oligotrophic state from 1999 to 2004 (~0.03 mg m
-3
over five years; see also Behrenfeld et al. 2006; 2008). The quantification of these small trends is the focus of this paper. Plots for other regions for the overlapping missions can be found in the supplemental information section.
Comparison of the monthly Chl
DW
time series among the three simultaneous global ocean color missions shows mean differences (normalized bias) ranging from 4.9% between SeaWiFS and
MERIS to 12.6% between SeaWiFS and MODIS-Aqua to 14.4% between MERIS and MODIS-

Aqua. Normalized root mean square (RMS) differences range from 12.6 to 45.7% with the lowest normalized errors occurring for the SeaWiFS-MODIS-Aqua pair (see supplemental section for more statistics and for other GlobColour regions).
Table showing CHlDW5050 with all bins vs common bins -> sampling
Validation against field measurements…
Table showing in situ comparisons
Summary – there are substantial differences among time series records of Chl
DW
; among multiyear mission records and between the individual mission records and field data.
The creation of a CDR requires the statement of a scientific question. Here, we ask whether a typical interannual trend in Chl
DW
can be detected given a set of satellite ocean color missions?
The SeaWiFS record is the longest record available to assess trends in Chl
DW
(Fig. 1b). As mentioned above, there a range of trends in Chl
DW
of interest ranging from seasonal changes of
0.02 mg m
-3
over a 6 month period to changes of ~0.03 mg m
-3
over a 5 year period (Fig. 1b).
For present purposes, we will assume that a lower bound trend of 0.002 mg m
-3
y
-1
change needs to be detected (that is a 0.02 mg m
-3
change over a time scale of 10 years).
Falkowski and Wilson (1992) analyzed changes in Secchi disk depths for the North Pacific basin looking from the early 1900’s to the 1980’s. They find regional trends in Secchi disk depths that correspond to changes in surface layer chlorophyll concentrations ranging from -0.004 to 0.006 mg m
-3
y
-1
(on 10 o
by 10 o
boxes) with overall basin scale changes of 0.002 mg m
-3
y
-1
(Falkowski and Wilson, 1992). These historical trends based upon the analysis of Secchi disk depths are consistent with the minimum bound trends suggested from the SeaWiFS Chl
DW
record.
DAVID A – please add a short paragraph on the CZCS/SeaWiFS experience for assessing trends. You know this literature better than I…
Compare to trends predicted from model forecasts of future climates (Sarmiento et al 2006
Boyd/Doney, Bopp et al., etc.),
The observational detection of global climate-driven trends in key Earth System functions (land, atmosphere, or ocean) commonly requires remote sensing data records with durations exceeding the typical lifetime of any given satellite system. Because of this attribute, successful construction of a CDR is dependent either on a mission launch sequence plan that minimizes the probability of a gap in observations or a robust approach for accurately bridging satellite records separated by gap periods. The preferred approach for establishing CDRs is the former, where a continuous, uninterrupted time-series of observations is achieved with significant overlap between missions to allow sensor inter-calibration/comparison (e.g., Fig. 1B). While it is impossible to eliminate the risk of a data gap altogether (i.e., there is always a finite probability

that every sensor in operation fails before a new mission is launched), it is possible to develop a mission launch scheme aimed at reducing gap risk to an acceptably low level. Such gap minimization launch plans have been investigated for a few Earth science CDR observations
(e.g., clouds). Here, we conduct an analogous gap assessment for ocean color CDRs directly following these earlier approaches from other disciplines.
The context of our analysis was to assess the probability of an observational data gap over a 20 year CDR period. For illustration, we begin with a series of hypothetical scenarios, each initiated with the launch of two sensors in year 1, followed by a variable number of subsequent launches over 20 years (Fig. 2A). For each scenario, we assume mission design lifetimes of 7 years and that each sensor successfully delivers observational data of sufficient quality for climate research. Following earlier assessments and based on historical mission statistics (Wielicki), we adopt an acceptable CDR gap risk goal of

10% and consider mission failure probabilities for launch (3% probability, first year only), from sensor failure (4.7% probability each year during prime lifetime and 9.4% thereafter), and from satellite system failure (0.8% probability each year during prime lifetime and 1.6% thereafter).
The simplest scenario considered for achieving a 20 CDR was the launch of only two sensors in year 1. In this case, the probability of experiencing a gap in the data record exceeded the

10% threshold within 7 years and reached 72% by year 20 (red line in Fig. 2A). Our second scenario entailed additional mid-CDR launches occurring on a 7 year period, but this sequence does little to maintain risk below the

10% level beyond the simpler 2-mission scenario, although it does reduce the 20 year risk by nearly half (39%) (orange line in Fig. 2A). To understand this result, one must consider the question being asked by this assessment, “What is the probability of a gap in observational data from the beginning of a CDR record to a 20 year goal.” The nature of this question imposes the property on any given risk trajectory that addition of new missions can only decrease the rate at which risk increases, but can never cause risk to decrease over time
1
. In the third scenario, mid-CDR launches are assumed on a 5 year period, which substantially lengthens the period during which gap risk remains below 10% and reduces overall risk to 22% for the 20 year period (olive line in Fig. 2A). Continuing these scenarios, we find that a launch cycle of once very 3 years is required to keep risk below the assumed goal, although a 4-year mission cycle is nearly adequate (blue and green lines in Fig.
2A, respectively).
The hypothetical scenarios thus described can now be compared to real trajectories for current and planned ocean color sensors (Fig. 2B). For this comparison, we first consider USonly ocean color missions and then the full international suite of sensors listed in Table 1. In these cases, we assess the probability of a gap in a 20 year ocean color CDR beginning from the
1997 launch of the first climate-quality sensor, SeaWiFS. This difference from the above hypothetical scenarios has a significant impact on the form of each risk trajectory: from 1998 to the present, the probability of a data gap is 0% because the observations have already been
1 As an analogy to help understand this result, consider the following. Say you wanted to assess the probability of catching a particular flu virus over the course of the next 20 years. If the probability of catching this flu today is 1% per year, then there is a 5% chance you will catch the flu by the end of the 5 th year. Even if a vaccine is discovered in year 5 that reduces the risk of catching the flu to 0% per year, the overall risk for the 20 year period will not decrease but rather remain at the 5% risk accrued over the first 5 years. The same is true for the mission scenario illustrated in Figure 2A. If the risk of a data gap rises to 10% by year 7, then the probability of a gap for the 20 year period can never be below this 10% value even if a new mission was launched each week.

collected and, consequently, risk can only increase from the present to the end of the 20 year period. In addition, we employ actually design lifetimes for each mission, rather than adopting a uniform value of 7 years, and we assume that the SeaWiFS will not exceed 2012 (this assumption was made because the SeaWiFS mission has been frequently going into safe-hold mode since the beginning of 2008).
Within the US-only scenarios, our risk assessment for the SeaWiF-MODIS sensors alone indicates that the probability of a data gap will exceed the

10% goal by 2010 (red line in Fig.
2B). The launch of VIIRS on NPP in 2010 (Table 1) extends the sub-threshold risk period by only a single year, but reduces the 20 year gap risk from 69% for SeaWiFS-MODIS only to 33%
(orange line in Fig. 2B). Subsequent launch of VIIRS on NPOESS in 2013, in contrast, does little to improve the risk trajectory over the VIIRS on NPP scenario (note that this result is strongly influenced by our assessment restriction to 20 years. If we were to extend this assessment to 25 or more years, gap risk would be significantly reduced by launching VIIRS on
NPOESS). An important aspect of the US-only scenarios shown in figure 2B is that we are assuming each of the sensors considered will deliver climate-quality data. In truth, all indications to date suggest that the VIIRS on NPP will not meet climate-quality data requirements. Likewise, current plans for VIIRS on NPOESS do not include regular lunar viewing for characterization of sensor degradation. These lunar observations have been demonstrated by SeaWiFS as essential for climate-quality data acquisition. Consequently, the real ocean color CDR gap risk probability for US-only missions is best represented by the
SeaWiF-MODIS alone scenario indicated by the red trajectory in figure 2B with enlarged symbols and an emboldened line. Improving this scenario will require the near-term launch of a gap filler mission designed around lessons learned from heritage missions to ensure climatequality data retrieval.
While the US-only scenario for a 20 year ocean color CDR is bleak, a more promising trajectory emerges when considering the full suite of international ocean color missions (Fig.
2C). Including all current and planned sensors from SeaWiFS forward, we find that the probability of a data gap remains well below the

10% goal throughout the 20 year period. As in the US-only case, though, this result assumes that each of these planned missions can provide climate-quality data (note that VIIRS on NPP and NPOESS are included in this trajectory). If this were the case, than gap risk does not represent the greatest threat to an ocean color CDR.
Instead, the primary challenges for achieving a 20 year climate record will be (1) assuring free and open global access to data from each and every ocean color mission and (2) achieving successful data merging between sensors. The 20 year ocean color record is thus likely to look very much like an extension of the SeaWiFS-MODIS-MERIS time series shown in figure 1B, where the key challenge is achieving an effective and robust merger of data set for overlapping periods.
The construction of an ocean color CDR must consider how expected gaps in the satellite record and post-gap differences in satellite data quality will affect the achievement of meeting the goals of the CDR. Here ,we model the relative importance of gap length and relative post-gap data quality on achieving CDR goals for ocean color data. The approach is to model expected

variability in the observable field and to perform a series of statistical experiments altering gap length and simulated expected changes in post-gap data quality; 1) a random bias (or offset) between the pre- and post-gap records and 2) point-to-point noise relative to the pre-gap data set.
The SeaWiFS global deep water average chlorophyll concentration, Chl
DW
, is used as a basis for the modeling and to illustrate the challenges in creating an ocean color CDR with the goal of detecting trends in Chl
DW
projected to the year 2018 (10 years from the writing of this paper).
The first step is to create a collection of random time series of Chl
DW
as the basis of the scenario evaluations. To do this, the long-term mean and an annual harmonic are removed from the
Chl
DW
time series and the residual signal is modeled using an autoregressive (AR) model (lpc.m in MATLAB) with 36 components. The number of components spans time scales from 1 month to 3 years and to a good job representing the residual of the (see Supplemental Figure 4). The
AR components are then used to simulate a set of time series of Chl
DW
(after adding in the mean and the annual harmonic) from which a variety of numerical experiments can be performed. The results of the AR modeling are shown in supplemental figures 4.
An example of the trend quantification is shown in Figure 3 for an ensemble of 100 different projections of Chl
DW
and an assumed minimum trend of 0.002 mg m
-3
y
-1
. The upper panel of
Figure 3 shows estimates of the trend for each realization along with 95% confidence intervals for the trend estimate while the lower panel shows the 100 realizations of the Chl
DW
time series.
The results show that the minimum trend can be assessed from each Chl
DW
time series and in only one case do the 95% confidence intervals do not include the assumed trend.
This type of analysis can be repeated in an effort to assess the relative importance of a gap in the satellite data record and the influence of post-gap data quality. The effect of adding a 5 year gap starting in 2008 on the quantification of long term trend is shown in Figure 4. This shows that the assumed trend (0.002 mg m -3 y -1 ) can be successfully estimated even if there is a 5 year gap in the record when the post-gap data are of the same quality as the pre-gap data. The trends are still well predicted after adding 10% random noise to the post-gap data (Figure 5). However, the extents of the 95% confidence intervals are much larger than without the post-gap noise and a few (3) of the trend estimates do not include the assumed trend value. Last, the effect of a random bias of the post-gap data, such as would occur to calibration issue among satellite missions, is explored (Figure 6; note the large difference in the vertical scale of this plot). Here, each post-gap data set is subjected to a 10% random bias (with no random month-to-month noise) and the trends are retrieved for each realization. We find that the spread among estimates is much larger where only a few confidence intervals include the imposed trend and many do not even predict a positive slope value. Clearly, understanding the effects of random biases between missions is of first order importance for creating an ocean color CDR.
The combined effects of satellite data gap length, random noise and random inter-mission bias on the ability to detect trends in Chl
DW
are presented in figures 7 and 8. Here, the minimum detectable trends in Chl
DW
are determined by assessing the smallest trend where less than 5% of
300 realizations include zero within the 95% confidence intervals for their linear trends. In figure
7 is shown the relationship between random month-to-month noise and random inter-mission bias on the minimum detectable trend in Chl
DW
for the case of a 5 year gap starting in 2008. In figure 8 is shown the relationship between gap length and random inter-mission bias on the minimum detectable trend in Chl
DW
for the case with 10% random, post-gap noise. Clearly, the

effects of post-gap bias is the most important factor limiting the ability to detect non-zero trends in the ChlDW time series.
Conclusions: Inter-satellite biases set what minimum trends that can be determined from a CDR.
Within reason [which needs TBD], noise and gap length are 2 nd order effects. DV needs to edit this and finish …
What about the levels of uncertainties among the data sets? Talk about stats in supplemental table 1. Get to the point that we relate our intermission skills to the gap/quality analysis presented. Conclude: We need to think of other ways of doing this…
Stéphane – Can you take the lead on this? Use SeaWiFS global deep water Chl obs to assess our abilities to quantify expected uncertainties in OC CDR’s given differences in sampling of ocean through a cloudy atmosphere. Figure 8 shows number of days for 2003 where SeaWiFS, MERIS and MODIS-Aqua made good ocean color observations.
Make a table showing the expected uncertainties in creating time series for the GlobColour regions for daily, 8 day and monthly time scales. Also summarize into a figure??
Anyone wanna take a swipe at this? I can finish it later – just think we need to talk about this.
The concept of fundamental/thematic CDR’s is just too sloppy to be useful in reality.
The NRC (2004) report developed the concept of fundamental and thematic CDR’s in an attempt to constrain the number of CDR’s that would be created by a single type of satellite observation.
A fundamental CDR is defined as a satellite measured quantity (i.e., satellite radiance, waterleaving radiance) while thematic CDR’s are geophysical quantities such as the chlorophyll a concentration or the spectral absorption of light by colored dissolved organic matter. As described previously, a thematic CDR can be determined using a scientific question to constrain what is sufficient to create a CDR. However, it is unclear how to define sufficiency for a fundamental CDR as these quantities are not used themselves for asking climate science questions. Hence, one to know ahead of time the future science questions that a fundamental
CDR will be used for. Here, we have only addressed the construction of thematic CDR. The creation of a fundamental CDR requires the step of conversion to a thematic CDR with known procedures to insure it will be able to sufficiently answer scientific questions.
Need to talk about radiometric and modeling independence issues in making fundamental CDR’s
Chuck: wanna take a stab at this based upon our discussions??

We’re screwed. We likely won’t have the assets in space and following our existing single mission analysis procedures we will not likely be able to do this well. Go thru this step by step – reiterating the results presented above.
There is some hope in merging – GlobColour results??? – but still the bias issue remains. Need to think about how to fix this. The paths of dealing with the bias correction requires us to consider 4 basic paths…

Inter-mission bias corrections on gain settings

Use of field sites (ala Boussole and MOBY) to interpolate missions

Blending of field data into satellite imagery via optimal interpolation

Full data assimlation using models
Lay out the potential path and pitfalls…
Not sure if we’re ready to write this yet. I have some ideas below – but I think we need to get the rest of this crafted…
Need new paradigms in OC cal/val for OC CDR production. Single satellite views do not cut it.
Bias reduction must be key. BUT need to start at the TOA satellite radiance level to achieve this.
This means pre-launch, on-orbit calibration data must be used in this process.
Given international nature of OC assets, this pushes the international OC research community to new levels of cooperation, etc. Give suggestions for this – open data policies, centralized distribution of OC CDRs, personnel exchanges among satellite teams, etc.
Restate what we mean by a CDR just in case they do not get it.
Restate the issues as they stands and ideas of what is required to go forward
Make a list of recommendations and construct a List of recommendations!!!!
Two lists – Getting good data & What are needed to paste them together

:
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Behrenfeld, M.J., R.T. O’Malley, D.A. Siegel, C.R. McClain, J.L. Sarmiento, G.C. Feldman,
A.J. Milligan, P.G. Falkowski, R.M. Letelier & E. S. Boss, 2006: Climate-driven trends in contemporary ocean productivity. Nature , 444 , 752-755.
Chavez, F.P., J. Ryan, S.E. Lluch-Cota, and M. Ñiquen C., 2003: From anchovies to sardines and back: Multidecadal change in the Pacific Ocean. Science , 299 , 217-221.
Falkowski, P.G., and C. Wilson, 1992: Phytoplankton productivity in the North Pacific Ocean since 1900 and implications for absorption of anthropogenic CO
2
. Nature 358 , 741-743.
Fougnie, B., P. Henry, A. Morel, D. Antoine, and F. Montagner, 2002: Identification and
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Calibration of Space Sensors over Rayleigh Scattering. Ocean Optics XVI, Santa Fe, NM,
November 18-22, 2002.
Franz, B., 2005, Methods for Assessing the Quality and Consistency of Ocean Color Products, http://oceancolor.gsfc.nasa.gov/DOCS/methods/sensor_analysis_methods.html
GlobColour, 2007: GlobColour Full Validation Report, version 1.1, December 14, 2007, http://www.enviport.org/globcolour/validation/report/GlobCOLOUR_FVR_v1.1.pdf
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Blending in situ and satellite data for the Coastal Zone Color Scanner era, J. Geophys. Res.
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106 , 2499–2515.
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Table 1: Global Ocean Color Mission Characteristics (from IOCCG webpage – May 2008)
Sensor Mission Agency # Bands
CZCS Nimbus-7 /
USA
NASA / USA
Relevant
Dates
December
1978 –
July 1986
Swath
(km)
1556
Resolution
(m)
825 6
OCTS
SeaWiFS
ADEOS-1
/ Japan
Orbview-2
/ USA
NASDA / Japan
NASA / USA
November
1996 –
July 1997
September
1997 – present
1400
2806
700
1100
12
8
MERIS Envisat /
EU
MODIS Aqua/EOS-
PM / USA
OCM-2 Oceansat-2
/ India
VIIRS NPOESS-
C1 / USA
S-GLI GCOM-C /
Japan
ESA / EU
NASA / USA
ISRO / India
April 2002
- present
July 2002 - present
1150
2330
September
2008
1420
June 2010 3000
300 / 1200
1000
15
36
1000-4000 8
VIIRS NPP / USA DoD/NOAA/NASA
/ USA
OLCI Sentinal-
3A / EU
ESA / EU October
2012
1120
DoD/NOAA / USA January
2013
JAXA / Japan
3000
Early 2014 1150 /
1400
370 / 740
250 / 1000
370 / 740
250 / 1000
22
19
22
19
OLCI Sentinal-
3B / EU
ESA / EU April 2015 1120 250 / 1000
Relevant dates are defined as dates of sample collection or projected date of launch.
19

Table 2: Comparison of Satellite Derived Deep Water Chl Observations
Will be constructed from the tables at the end of the supplementary section
Table 3: In Situ Validation of Deep Water Chl Observations
Will be modified…
Median
ratio
Median
%dif. N in situ min in situ max satmin satmax slope intercept r2 bias rms
Chl SeaWiFS 1,03 30,13 419 0,024 3,72 0,015 2,59 0,87 -0,13 0,81 0,08 0,48
Chl MODIS 0,96 22,12 90 0,039 2,68 0,030 1,84 1,00 -0,01 0,85 -0,01 0,41
Chl MERIS 1,19 46,40 152 0,06 6,70 0,059 14,48 0,65 -0,16 0,63 0,05 0,26
(SeaWiFS and MODIS from http://seabass.gsfc.nasa.gov/matchup_results.html
; MERIS clearly from a different dataset (Odile: please check))
Sean: Please update your (SeaWiFS and MODIS from http://seabass.gsfc.nasa.gov/matchup_results.html
; MERIS clearly from a different dataset (to be checked))

Figure 1: Monthly mean chlorophyll concentrations (Chl
DW
) for global deep water sites (> 1000 m) and constrained between 50 o
N and 50 o
S. Left panels shows all five global missions and the right panel shows just the three global missions with overlapping observation. (SeaWiFS,
MERIS and MODIS-Aqua).
Stephane – we need to update the left panel with the correct data!!

QuickTime™ and a
decompressor are needed to see this picture.
Figure 2: Reliability assessment for ocean color satellites. Need to update

Figure 3: Modeling of trend quantification assuming a trend of 0.002 mg m -3 y -1 . Upper panel: estimates of trend with 95% confidence intervals for 100 realizations of the Chl
DW
time series.
Lower panel: example projected Chl
DW
time series. Details of ensemble modeling in the supplementary information section.

Figure 4: Modeling of trend quantification assuming a trend of 0.002 mg m
-3
y
-1
and a gap of 5 years starting in 2008. Upper panel: estimates of trend with 95% confidence intervals for 100 realizations of the Chl
DW
time series. Lower panel: example projected Chl
DW
time series. Details of ensemble modeling in the supplementary information section.

Figure 5: Modeling of trend quantification assuming a trend of 0.002 mg m
-3
y
-1
, a gap of 5 years starting in 2008 and random noise of 10% relative to the pre-gap data set. Upper panel: estimates of trend with 95% confidence intervals for 100 realizations of the Chl
DW
time series.
Lower panel: example projected Chl
DW
time series. Details of ensemble modeling in the supplementary information section.

Figure 6: Modeling of trend quantification assuming a trend of 0.002 mg m -3 y -1 , a gap of 5 years starting in 2008 and a random bias of 10% relative to the pre-gap data set. Upper panel: estimates of trend with 95% confidence intervals for 100 realizations of the Chl
DW
time series.
Lower panel: example projected Chl
DW
time series. Details of ensemble modeling in the supplementary information section.

Figure 7: Minimum detectable trend in modeled Chl
DW
(in units of mg m
-3
y
-1
) as a function of postgap bias and noise levels for the case with a 5 year gap starting in 2008. Minimum detectable trends are determined by assessing the smallest trend in Chl
DW
do 95% of 300 realizations not include zero in the 95% confidence intervals for a linear trend.

Figure 8: Minimum detectable trend in modeled Chl
DW
(in units of mg m -3 y -1 ) as a function of gap length and post-gap bias levels for the case with postgap noise level of 10%. Minimum detectable trends are determined by assessing the smallest trend in Chl
DW
do 95% of 300 realizations not include zero in the 95% confidence intervals for a linear trend. Gaps are assumed to start in 2008.

Figure 8. Geographic distribution of the number of days of coverage in 2003 for A) SeaWiFS, B)
MERIS, C) AQUA and D) when the three sensors are merged.
Stephane: please redo figure with % days covered and without the merged figure – line them up vertically

Supplemental Information:

Fig. S1 - Regions from GlobColour Validation Program

Fig. S2 - Time series of the SeaWiFS, MERIS and MODIS-Aqua period for the
GlobColour validation regions

Fig. S3 - Log-log validation plots of field vs. satellite Chl for the three overlapping missions

Fig. S4 – Autoregression modeling of the residual Chl
DW
time series.

Table S1 - Intercomparison statistics among SeaWiFS, MERIS and MODIS-Aqua
Supplemental Figure 1: Validation regions from GlobColour (GlobColour, 2007)

Table S1 - Regions Description from GlobColour Validation Program
Abbreviation Lat / Long of Box
Corners
Long name
-180 90 180 -90 Global - Deep water Glob
DW
Glob
50DW
Hawaii
-180 50 180 -50
-158.5 19.9 -156.5 18.0
Latitude -50:+50 - Deep water
Hawaii
PacN
PacN45
PacN35
-180.0 23.0 -159.4 15.0 North Pacific
-170.0 50.0 -150.0 40.0 Pacific +40:+50
-170.0 40.0 -150.0 30.0 Pacific +30:+40
PacN25
PacN15
PacN05
PacEqu
PacS05
PacS15
PacS25
PacS35
PacS45
PacSE
Sarg
AtlN
AtlS
EAtl
Med
CMed
AtlN55
-170.0 30.0 -150.0 20.0
-170.0 20.0 -150.0 10.0
-170.0 10.0 -150.0 0.0
-170.0 10.0 -150.0 -10.0
-170.0 0 -150.0 -10.0
Pacific +20:+30
Pacific +10:+20
Pacific 0:+10
Equator Pacific
Pacific -10:0
-170.0 -10.0 -150.0 -20.0 Pacific -20:-10
-170.0 -20.0 -150.0 -30.0 Pacific -30:-20
-170.0 -30.0 -150.0 -40.0 Pacific -40:-30
-170.0 -40.0 -150.0 -50.0 Pacific -50:-40
-130.2 -20.7 -89.0 -44.9 South-East Pacific
-80.0 33.0 -70.0 23.0 Sargasso Sea
-62.5 27.0 -44.2 17.0
-32.3 -9.9 -11.0 -19.9
-16.0 40.0 -6.0 30.0
-6 46.5 36 30
14.0 41.0 24.0 31.0
-50.0 60.0 -20.0 50.0
North Atlantic
South Atlantic
East Atlantic
Mediterranean sea
Central Mediterranean sea
Atlantic +50:+60
AtlS55
IndS
WAust
Wmpl
PacNW
-20.0 -50.0 10.0 -60.0
89.5 -21.2 100.1 -29.9
112.0 -10.0 122.0 -20.0
150.0 27.5 160.0 17.5
139.5 22.7 165.6 10.0
Atlantic -60:-50
South India
West Australia
Wmpl
North-West Pacific
EAust 155.0 -23.0 165.0 -33.0 East Australia
Geolocation: order is minimum longitude, maximum latitude, maximum longitude, minimum latitude.
Deep water mask: where water depth is greater than 1000m ; applied everywhere.

Supplemental Figures 2: Monthly time series for SeaWiFS, MERIS and MODIS-Aqua for the
GlobColour validation regions in supplemental figure 1. All available observations within each region are averaged.

Sean/Odile – wanna take a stab at figure 3??
Supplemental Figures 3: Log-log validation plots of field vs. satellite Chl for the three overlapping missions (SeaWiFS, MERIS, MODIS-Aqua - columns) and globe (upper row) and the DW 50/50 region (lower row).

Supplemental Figure 4: Steps in autoregressive modeling of the residual Chl
DW
time series.
Upper left panel: Observed residual Chl
DW
(blue) and autoregressive modeled Chl
DW
time series
(green). The autoregression modeled Chl
DW
time series is created using the lpc.m function in
MATLAB using 36 components (time scales of 1 month to 3 years). Upper right panel: Lagged correlation between observed and autoregressive modeled Chl
DW
time series. Note significant correlations are observed only at zero lag. Lower left panel: Projected Chl
DW
time series for 100 realizations including the annual harmonic (in mg m
-3
). Lower right panel: Normalized projected
Chl
DW
time series (in %). Normalization is performed by subtracting off the annual harmonic fit and then dividing by it.

AtlN55
AtlS55
PacN45
PacN35
PacN25
PacN15
PacN05
PacS05
PacS15
PacS25
PacS35
PacS45
Sarg
Region
Glob
Glob50
Glob50DW
Hawaii
PacN
PacNW
PacSE
AtlN
AtlS
IndS
Wmpl
EAust
WAust
Med
CMed
EAtl
PacEqu
Supplemental Table 1: Statistical Comparison Among the SeaWiFS, MERIS and MODIS-Aqua
Regional Chlorophyll Retrievals ALL BINS MONTHLY OBS
12.0
21.2
25.6
22.1
15.9
16.0
17.9
13.0
11.4
11.0
19.7
26.2
12.5
Mean
Error
(%)
RMS
Error
(%)
SEAWIFS-AQUA
N
11.6
12.6
12.6
23.5
1.8
12.6
12.6
35.5
57
57
57
56
24.8
20.2
22.7
22.1
21.8
20.7
55.7
64.0
20.3
4.2
3.6
2.7
57
57
57
57
57
56
19.4
16.5
11.9
7.4
13.1
9.8
12.3
0.7
0.7
8.2
1.3
12.0
29.2
1.8
7.7
10.5
6.9
4.7
2.5
2.2
9.4
0.7
1.1
22.7
1.7
3.7
4.6
57
57
56
57
56
56
57
57
57
57
57
57
57
52
41
57
57
57
57
57
14.9
11.8
13.6
9.5
5.7
10.5
13.6
11.6
10.8
9.9
10.3
11.1
31.2
Mean
Error
(%)
RMS
Error
(%)
SEAWIFS-MERIS
N
12.0
71.5
4.9
9.0
11.7
47.6
45.7
34.2
57
57
57
56
11.2
9.9
17.1
7.2
7.3
8.9
49.2
61.9
51.8
26.3
7.5
3.3
57
57
57
57
57
56
7.1
8.1
8.4
44.2
8.7
25.8
10.6
5.9
4.9
39.8
6.7
9.3
33.8
11.0
30.2
28.9
10.5
8.8
13.1
14.4
10.8
2.1
3.3
38.5
5.3
8.5
19.6
57
57
56
57
56
56
57
57
57
57
57
57
57
52
41
57
57
57
57
57
24.4
24.0
19.4
17.4
26.1
27.0
29.8
5.9
37.7
45.8
34.3
22.9
18.2
Mean
Error
(%)
RMS
Error
(%)
AQUA-MERIS
19.8
97.9
14.4
23.6
11.9
42.6
30.3
31.3
24.4
26.3
43.4
27.1
26.8
32.8
29.1
69.6
27.6
56.8
5.5
3.6
24.7
22.3
15.7
41.1
15.7
20.3
26.5
13.6
1.1
2.5
68.1
4.7
11.1
14.5
28.0
20.3
7.7
3.9
8.3
10.8
3.8
4.8
30.4
4.4
15.0
35.5
8.5
N
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
54
49
41
54
54
54
54
54
54
54
54
54
Note: Mean error is defined as 100*(Mean_mission1-Mean_mission2)/ Mean_mission1.

Monthly time series statistics – common bins
Region
Glob
Glob
DW
Glob
50
Glob
50DW
Hawaii
PacN
PacN45
Mean
Error
(%)
RMS
Error
(%)
SEAWIFS-AQUA
N
9.7
13.0
7.3
12.1
23.5
24.9
10.5
41.6
18.3
14.8
62.5
79.5
66
66
66
66
66
66
Mean
Error
(%)
RMS
Error
(%)
SEAWIFS-MERIS
N
2.8
0.4
9.3
43.9
Mean
Error
(%)
RMS
Error
(%)
AQUA-MERIS
66 -7.6 17.0
66 -14.5 47.5
N
66
66
-2.0
-1.1
6.8
8.7
20.0
20.4
53.7
57.1
66
66
66
66
-10.1
-15.1
-21.8
-21.5
26.1
28.4
55.9
59.7
66
66
66
66
PacN35
PacN25
11.0
10.9
19.6
16.3
2.2
25.4
66 -2.4
66 -8.3
66 -0.9
53.2
7.3
17.3
66 -15.0 38.1
66 -21.6 6.4
66 -25.4 30.7
66
66
66
PacN15
PacN05
PacEqu
PacS05
PacS15
PacS25
PacS35
26.0
11.4
11.9
14.8
21.1
24.6
21.4
55.0
7.5
14.0
14.6
18.9
18.3
11.2
66
66
66
9.7
-6.8
-6.7
66 -5.3
66 -1.1
66
66
-0.8
-1.3
40.5
13.1
29.7
29.1
48.8
18.3
11.2
66
66
66
66
-22.1
-20.5
-21.1
-28.8
58.2
10.4
21.6
66 -23.6 21.2
66 -28.1 42.4
66 -33.7 20.5
7.6
66
66
66
66
66
66
66
PacS45
PacSE
Sarg
AtlN
AtlS
EAtl
Med
14.4
21.0
16.0
23.0
21.4
11.0
13.4
4.6
24.9
3.9
9.4
8.2
2.2
3.7
66 -4.3
66 -4.0
66 -5.2
66
66
4.9
1.3
66 -8.9
66 -2.3
17.5
21.0
3.7
12.3
15.2
8.8
15.0
66 -21.8 12.2
66 -31.6 25.3
66 -25.1 7.0
66 -23.5 14.6
66 -25.6 12.6
66 -22.4 8.2
66 -18.1 11.4
66
66
66
66
66
66
66
CMed
AtlN55
AtlS55
IndS
WAust
Wmpl
PacNW
EAust
13.6
17.8
11.8
20.0
12.3
19.2
19.9
15.3
4.4
2.6
2.2
6.2
2.2
19.8
67.4
1.5
66 -7.1
58 5.6
50 -4.0
66 -4.9
66 -7.2
66 0.2
66 3.1
66 -2.9
9.0
10.2
7.1
6.9
5.4
17.9
65.3
3.8
66 -24.0
58 -14.8
50 -17.9
66 -31.1
66 -22.3
66 -23.5 30.3
66 -21.1 80.6
66 -21.5 2.7
9.9
6.7
9.2
8.4
3.3
66
58
50
66
66
66
66
66