Achieving Global Ocean Color Climate Data Records ASLO Aquatic Sciences Meeting 17 February 2011 –...

Achieving Global Ocean Color Climate Data Records

ASLO Aquatic Sciences Meeting

17 February 2011 – San Juan, Puerto Rico

Bryan A. Franzand the

NASA Ocean Biology Processing Group

A climate data record is a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change.

U.S. National Research Council, 2004

What is a Climate Data Record?

1980 200019901985 201020051995

Length & continuity achieved via multiple missions

SeaWiFS (NASA)CZCS (NASA)

MODIS-Terra (NASA)

MERIS (ESA)

MODIS-Aqua (NASA)

OCM2 (ISRO)

IOCCG 2010

How do we achieve consistency?

• Focus on instrument calibration – establishing temporal stability within each mission

• Apply common algorithms– ensuring consistency of processing across missions

• Apply common vicarious calibration approach– ensuring spectral and absolute consistency of water-leaving radiance

retrievals under idealized conditions

• Perform detailed trend analyses (hypothesis testing)– assessing temporal stability & and mission-to-mission consistency

Trophic Subsets

Deep-Water (Depth > 1000m) Oligotrophic (Chlorophyll < 0.1)

Mesotrophic (0.1 < Chlorophyll < 1) Eutrophic (1 < Chlorophyll < 10)

How do we achieve consistency?

• Concentrate on instrument calibration – establishing temporal stability within each mission

• Apply common algorithms– ensuring consistency of processing across missions

• Apply common vicarious calibration approach– ensuring spectral and absolute consistency of water-leaving radiance

retrievals under idealized conditions

• Perform detailed trend analyses (hypothesis testing)– assessing temporal stability & and mission-to-mission consistency

• Reprocess multi-mission timeseries– incorporating new instrument knowledge and algorithm advancements

Latest NASA Reprocessing

Highlights:• incorporated sensor calibration updates**• regenerated all sensor-specific tables and coefficients• improved aerosol models based on AERONET• updated chlorophyll a and Kd algorithms based on NOMAD v2

Status:• MODISA completed April 2010 (update in progress)• SeaWiFS completed September 2010• OCTS completed September 2010• MODIST completed January 2011• CZCS in progress

Scope: MODISA, MODIST, SeaWiFS, OCTS, CZCS

http://oceancolor.gsfc.nasa.gov/WIKI/OCReproc.html

SeaWiFS & MODISA Rrs in good agreementDeep-Water

solid line = SeaWiFS R2010.0dashed = MODISA R2009.1

Rrs

(st

r-1)

412

443

488 & 490

510531547 & 555

667 & 670

within 5%at all times

Mean spectral differences agree with expectations

SeaWiFS MODISA

oligotrophicmesotrophiceutrophic488

490

547 & 555

Variability in SeaWiFS & MODIS/Aqua Rrs timeseries are similar in all trophic subsets

Rrs (443) Rrs (55X)

deep wateroligotrophicmesotrophiceutrophic

MODISA Rrs showing late-mission drift

412

443

488-490

510

531

Deep-Water

solid line = SeaWiFS R2010.0dashed = MODISA R2009.1

Rrs

(st

r-1)

MODISA to be reprocessedfrom at least 2009 onward

MODIST & MERIS vs SeaWiFS Rrs

ESA 3rd reprocessing of MERIS underway. First calibration update since 2006.

ESA OC-CCI plan to reprocess MERIS with NASA common algorithms.

Formal arrangments for NASA-ESA data exchange in progress.

MODIST & SeaWiFS MERIS & SeaWiFS

OCL-off

cal model extrapolation

The Multi-Mission Data Record

SeaWiFS SeaWiFS

MODIS/Aqua MODIS/Aqua

Fall 2002 Fall 2008

The Multi-Mission Data Record

SeaWiFS SeaWiFS

MODIS/Terra MODIS/Terra

Fall 2002 Fall 2008

Global Chlorophyll Timeseries

Oligotrophic Subset

Mesotrophic Subset

SeaWiFS

SeaWiFS


Oligotrophic Subset

Mesotrophic Subset

SeaWiFS MODISA

SeaWiFS MODISA


Oligotrophic Subset

Mesotrophic Subset

SeaWiFS MODISA MODIST


before reprocessing

before reprocessing


Oligotrophic Subset

Mesotrophic Subset

SeaWiFS MODISA MODIST MERIS


Comparison of variability in Chlorophyll Timeseries

SeaWiFS to MODISA SeaWiFS to MODIST

SeaWiFS to MERIS

deep wateroligotrophicmesotrophiceutrophic

Global Chlorophyll Anomaly Timeseries

Oligotrophic Subset

Mesotrophic Subset

SeaWiFS MODISA

SeaWiFS MODISA


Oligotrophic Subset

Mesotrophic Subset



5%, ± 0.003 mg m-3

Summary

• SeaWiFS is the first decadal-scale climate data record for ocean chlorophyll and, by proxy, phytoplankton biomass.

• MODIS/Aqua open-ocean timeseries in very good agreement– monthly reflectances agree to with 2% on average, 5% at all times

– chlorophyll variability is well correlated (90-95%) and equivalent in scale

– revised calibration model / reprocessing needed to fix late mission trends

• MODIS/Terra in much better agreement with SeaWiFS & MODISA after reprocessing, but after extensive cross-calibration to SeaWiFS– not an independent climate data record

• Instrument degradation is the primary challenge to development of ocean color climate data records. – use additional caution when interpretting data from recent years

New Missions

NPP/VIIRSOct 2011 launch

Oceansat-2/OCM-2Sep 2009 launch

OCM-2 Monthly Chlorophyll

limited on-board recording capacity and bi-annual tilt restrict sampling

ISRO data distribution: http://218.248.0.134:8080/OCMWebSCAT/html/controller.jspNASA test products: http://oceancolor.gsfc.nasa.gov/cgi/l3

1980 200019901985 201020051995

Length & continuity achieved via multiple missions

SeaWiFS (NASA)CZCS (NASA)

MODIS-Terra (NASA)

MERIS (ESA)

MODIS-Aqua (NASA)

OCM2 (ISRO)

IOCCG 2010

VIIRS (USA)

Different Instruments Designs

SeaWiFS

• 8 spectral bands (412-865nm)

• sufficient signal-to-noise• lunar calibration capability• tilt to minimize glint• very low polarization sensitivity• rotating telescope

• out-of-band response• straylight issues• subsampled global coverage

MODIS/Aqua

• 36 spectral bands (412-2130nm)

• increased signal-to-noise• reduced out-of-band response• global 1km coverage

• significant polarization sensitivity

• greater sunglint losses (no tilt)• multiple detectors (striping)• rotating, exposed scan mirror

(greater optical degradation)

Outline

Development of an ocean color CDR

Assessment of data quality

Future directions

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