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Achieving Global Ocean Color Climate Data Records ASLO Aquatic Sciences Meeting 17 February 2011 – San Juan, Puerto Rico Bryan A. Franz and the NASA Ocean.

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Presentation on theme: "Achieving Global Ocean Color Climate Data Records ASLO Aquatic Sciences Meeting 17 February 2011 – San Juan, Puerto Rico Bryan A. Franz and the NASA Ocean."— Presentation transcript:

1 Achieving Global Ocean Color Climate Data Records ASLO Aquatic Sciences Meeting 17 February 2011 – San Juan, Puerto Rico Bryan A. Franz and the NASA Ocean Biology Processing Group

2 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?

3 Length & continuity achieved via multiple missions SeaWiFS (NASA) CZCS (NASA) MODIS-Terra (NASA) MERIS (ESA) MODIS-Aqua (NASA) OCM2 (ISRO) IOCCG 2010

4 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

5 Trophic Subsets Deep-Water (Depth > 1000m)Oligotrophic (Chlorophyll < 0.1) Mesotrophic (0.1 < Chlorophyll < 1) Eutrophic (1 < Chlorophyll < 10)

6 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

7 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

8 SeaWiFS & MODISA Rrs in good agreement Deep-Water solid line = SeaWiFS R dashed = MODISA R Rrs (str -1 ) & & & 670 within 5% at all times

9 Mean spectral differences agree with expectations SeaWiFS MODISA oligotrophic mesotrophic eutrophic & 555

10 Variability in SeaWiFS & MODIS/Aqua Rrs timeseries are similar in all trophic subsets Rrs (443)Rrs (55X) deep water oligotrophic mesotrophic eutrophic

11 MODISA Rrs showing late-mission drift Deep-Water solid line = SeaWiFS R dashed = MODISA R Rrs (str -1 ) MODISA to be reprocessed from at least 2009 onward

12 MODIST & MERIS vs SeaWiFS Rrs ESA 3 rd reprocessing of MERIS underway. First calibration update since ESA OC-CCI plan to reprocess MERIS with NASA common algorithms. Formal arrangments for NASA-ESA data exchange in progress. MODIST & SeaWiFSMERIS & SeaWiFS OCL-off cal model extrapolation

13 The Multi-Mission Data Record SeaWiFS MODIS/Aqua Fall 2002Fall 2008

14 The Multi-Mission Data Record SeaWiFS MODIS/Terra Fall 2002Fall 2008

15 Global Chlorophyll Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS

16 Global Chlorophyll Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS MODISA

17 Global Chlorophyll Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS MODISA MODIST before reprocessing

18 Global Chlorophyll Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS MODISA MODIST MERIS

19 Comparison of variability in Chlorophyll Timeseries SeaWiFS to MODISASeaWiFS to MODIST SeaWiFS to MERIS deep water oligotrophic mesotrophic eutrophic

20 Global Chlorophyll Anomaly Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS MODISA

21 Global Chlorophyll Anomaly Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS MODISA MODIST  5%, ± mg m -3

22 Global Chlorophyll Anomaly Timeseries Oligotrophic Subset Mesotrophic Subset SeaWiFS MODISA MODIST MERIS  5%, ± mg m -3

23 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

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27 New Missions NPP/VIIRS Oct 2011 launch Oceansat-2/OCM-2 Sep 2009 launch

28 OCM-2 Monthly Chlorophyll limited on-board recording capacity and bi-annual tilt restrict sampling ISRO data distribution: NASA test products:

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30 Length & continuity achieved via multiple missions SeaWiFS (NASA) CZCS (NASA) MODIS-Terra (NASA) MERIS (ESA) MODIS-Aqua (NASA) OCM2 (ISRO) IOCCG 2010 VIIRS (USA)

31 Different Instruments Designs SeaWiFS 8 spectral bands ( nm) 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 ( nm) 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)

32 Outline Development of an ocean color CDR Assessment of data quality Future directions


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