# Presentation: 142952

## Ocean Color Time Series Project

-       NASA REASoN CAN

- Goal:
- Provide consistent, seamless time series of Level-3 ocean color
- 	data from 1979, with a 9-year gap (1987-1996)
- Emphasize consistent algorithms and calibration
- 	methodologies
- Produce Climate/Earth Science Data Records (CDR/ESDR) of
- 	ocean color
- Make CDR’s available to the public

## CDR:  A time series of sufficient length, consistency, and continuity

- 		all temporal sensor artifacts removed
- 		no obvious interannual discontinuities unattributable to
- 			natural variability
- 		all known mission-dependent biases removed or quantified
- 		similar data quality and structure

- to determine climate variability and change
- National Research Council, 2004

## 1980

- 1990

- 2000

- 2010

- Ocean Color Satellite Missions:
- 1978-2010 and Beyond

- OCTS/
- POLDER

- SeaWiFS

- MODIS-Terra

- MODIS-Aqua

- VIIRS-NPP

- VIIRS-2

- “Missions to Measurements”

## Ocean Color Time Series

- REASoN CAN Team:
- Watson Gregg, NASA/Global Modeling and Assimilation:
- Oversight, Data Analysis
- DAAC:  Data Access and Visualization, Archival, Distribution
- Jim Acker, NASA/GES-DAAC
- Steve Kempler, NASA/GES-DAAC
- Greg Leptoukh, NASA/GES-DAAC
- OBPG:  Data Processing, In Situ Data Collection, Coincident Data
- Merging
- Gene Feldman, NASA/Ocean Color Processing
- Chuck McClain, NASA/Ocean Color Processing
- UCSB: Coincident Data Merging/ Data Technology
- Jim Frew, Stephane Maritorena, David Siegel

## Consistent Ocean Color Time Series Requires Similar

- 1)	Calibration
- 2)	Algorithms
- Spatial and Temporal Resolution (Level-3)
- 4)	Data Format
- 5)	Access
- Analysis Tools
- 7)	Bias characterization

## Slide 6

## SST: 		increased 0.2oC in 20 years

- 		= 1% change, or 0.05%/year
- 		Estimated from Reynolds using AVHRR
- SeaWiFS:	Maximum difference over lifetime
- 		(highest annual mean – lowest annual mean)
- 		= 5%
- Surface Air Temperature:
- 		increased 1oC in 100 years
- 		= 5% change, or 0.05%/year
- 		Estimated from thermometers, tree rings,
- 			ice boreholes

## The REASoN Project has now completed data records for

- OCTS (RV1)
- SeaWiFS (V5.1)
- MODIS-Aqua (V1.1)
- using consistent processing methodologies as defined

## Global Annual Trends using SeaWiFS, and SeaWiFS/Aqua

## Linear trends using 7-year average/composite images were calculated, and when significant (P < 0.05), shown here.  

- Linear trends using 7-year average/composite images were calculated, and when significant (P < 0.05), shown here.

- SeaWiFS

- SeaWiFS/Aqua

- Regional Annual Trends

## OCTS

- Nearly constant offset: about 15% _+ _1.5%

## Radiative

- Model
- (OASIM)

- Circulation
- Model
- (Poseidon)

- Biogeochemical
- Processes Model

- Winds       SST

- Layer Depths

- IOP

-      Ed(λ)
- Es(λ)

- Sea Ice

- NASA Ocean Biogeochemical Model (NOBM)

- Winds, ozone, relative humidity,
- pressure, precip. water, clouds
- (cover, τc), aerosols (τa, ωa, asym)

- Dust (Fe)

- Advection-diffusion

- Temperature, Layer Depths

- Ed(λ)
-      Es(λ)

- Global model grid: domain: 84S to 72N1.25 lon., 2/3 lat.14 layers

## Trends 1998-2004

- Data/Model			Linear Annual Trend
- SeaWiFS			-0.71% ns
- NOBM Free Run		 0.18% ns
- NOBM assimilation SeaWiFS	-0.98% ns

## Slide 14

## Compared to In situ Data

**SeaWiFS****	****       ****	****-1.3%****	****	****    32.7%****	****	****2086**
**Free-run Model****	****-1.4%****	****	****    61.8%****	****	****4465**
**Assimilation Model       0.1%****	****	****    33.4%****	****	****4465**

## Slide 16

## Advantages of Data Assimilation

- 	Achieves desired consistency, with low bias
- 	Responds properly to climatic influences
- 	Full daily coverage – no sampling error
- 	Effective use of data to keep model on track
- 	Only spatial variability required from sensors
- Disadvantages of Data Assimilation
- 	Low resolution (for now)
- 	No coasts (for now)
- 	Excessive reliance on model biases
- 	Cannot validate model trends with sensor data

## CDR:  A time series of sufficient length, consistency, and continuity

- to determine climate variability and change
- National Research Council, 2004

- Can the CZCS provide a Climate Data Record?

## CZCS

- The most ground breaking biological satellite in history
- More than 1000 peer-reviewed publications
- Major scientific advances
- 	Unprecedented view of spatial variability (gyres, coasts)
- 	Immenseness of the North Atlantic spring bloom
- 	Iron hypothesis
- 	Validation of first ocean biology models
- 	Importance of CDOM, aerosols, Case 1 and Case 2 waters
- 	Warm core rings, cold core rings
- 	Associations between tuna populations and fronts
- 	First data-driven estimates of global primary production
- 	First attempts at biological satellite data assimilation
- 	Established chlorophyll as a climate variable

## CZCS Deficiencies

- 1)	Low SNR
- 2) 	5 bands, only 4 of which quantitatively useful
- 		-- limits aerosol detection capability
- 3)	Navigation
- 4)	Polarization
- 5)	El Chichon
- 6)	Anomalous behavior post-1981
- 7)	Sampling

-  CZCS        OCTS      SeaWiFS     Terra	 Aqua        VIIRS

- 443 nm

- 490 nm

- 510 nm

- 555 nm

- 670 nm

- 765 nm

- 865 nm

- Ocean Color Missions: Bands

## CZCS Aerosol Workarounds

- Evans and Gordon, 1994, JGR
- Fixed aerosol type (epsilon)
- Advantages:	simple to implement
- 		led to data access, major advances in knowledge
- Disadvantages:	all variability assumed to be oceanic in origin
- 		single scattering aerosols
- 		underestimates of chlorophyll
- Gregg et al., 2002 Applied Optics
- Characterize aerosols in clear water, extrapolate using statistical 2-D
- methods (objective analysis), access SeaWiFS multiple scattering tables
- Advantages:	coincident
- 		variability partitioned among aerosols and ocean
- 		preserves knowledge derived from actual data
- 		2-D objective extrapolation
- 		multiple scattering aerosols
- 		clear water represents approx. 90% of oceans
- Disadvantages:	extrapolation is statistical
- 		requires aerosol fronts and chlorophyll fronts are uncorrelated

## Antoine et al., 2005, JGR

- Iterate between pre-defined optical representation in the ocean (Morel ocean)
- and atmosphere (Angstrom atmosphere) to obtain numerical convergence
- Advantages:	coincident
- 		preserves knowledge derived from actual data
- 		variability partitioned among aerosols and ocean
- Disadvantages:	single scattering aerosols
- 		only works in optically well-behaved areas
- REASoN V2, Sep. 2006
- Fixed aerosol model (maritime 99% humidity), CZCS-derived multiple
- scattering tables
- Advantages:	simple to implement
- 		multiple scattering aerosols
- Disadvantages:	all variability assumed to be oceanic in origin

## CZCS 

- CZCS
- Navigation

- 1989 data set

- Gregg et al.,
- 2002

## CZCS Polarization 

- Exists, and is tilt-dependent
- Gordon:  band-to-band polarization is removed through in situ calibration
- 	residual tilt-dependent polarization is maximum <1 digital count

## El Chichon

- Massive volcanic eruptions, late March 1982, early April 1982 (2)

## Slide 27

## Slide 28

## Slide 29

## Gordon and Castano 1988: up to tau = 0.4, effect = 1-2 digital counts at La(443)

- RMS

- Bias

- Comparisons with In situ Data in Spring

- Gordon and Castano 1988: up to tau = 0.4, effect = 1-2 digital counts at La(443)

## All missions have “events”

- SeaWiFS:
- 	3 El Ninos (1997-1998; 2002-2003; now)
- 	1 major 2.5-year La Nina (1998-2000)
- 	26 named tropical storms/hurricanes 2005
- 		(overall increased frequency and intensity of hurricanes)
- 	Canadian wildfires 2002
- 	Indonesian wildfire 1997
- 	Largest fires in history in Alaska 2004
- 	Biomass burning in Africa and South America
- 	Asian Brown Cloud
- 	Great Saharan Dust Storm 2004, 2006

## Slide 32

## CZCS Sampling

## Time Series Issues

- How calibrate historical and future sensors, maintaining consistency?
- 2)	Is BRDF a good idea?
- 3)	Can we define more rigorous metrics than in situ comparisons, that
- 		constrain global mean estimates?
- 4)	Is it acceptable to have two data streams:
- 		operational (best available methods; mission-dependent)
- 		climate (maximum commonality/consistency of methods)?
- 5)	How much consistency can we achieve without resorting to post-
- 		processing methods (blending of in situ data, assimilation)?
- 		Is this necessary?

## Gregg and Conkright, 2002

## Seven major papers on CZCS as a CDR

- 	Gregg et al., 2001 Applied Optics
- 	Gregg and Conkright, 2002, Geophysical Research Letters
- 	Merico et al., 2003, Geophysical Research Letters
- 	Gregg et al., 2003, Geophysical Research Letters
- 	Robinson et al., 2004, Canadian Journal of Remote Sensing
- 	Smyth et al., 2004, Geophysical Research Letters
- 	Antoine et al., 2005, JGR
- Question is not “can it be done” but “how well”

## Propose Ocean Color Climate Data Products

- Distinct from Operations Data Sets managed by OGBP
- Stored at DAAC, access using Giovanni
- Clear descriptions of data set and distinctions from operations data
- L3 format, 25-km, monthly, consistent with other climate data sets
- Includes discontinuous time series
- 	1978-1986
- 	1996-2004
- 	chlorophyll only for now
- mission names not mentioned except under detailed information

## Facilitates new and post-processing advances to ensure CDR

- 	consistency
- Two tries to get it right, then freeze data set for about 5 years
- 	to maximize climate use and stability (6 re-processings in
- 	9 years for SeaWiFS, another one planned soon; 2 in 2 years for
- 	Aqua)
- Does not interfere with operations requirements and community

## Gregg and Conkright, 2002

-                   vs.
-     Antoine et al., 2005

- 	-- 6% decline, associated with changes in other climate variables
- 		(SST, winds, iron)
- 	-- agreed with ocean biological and dynamical principles
- 		estimated from these climate variables
- 	-- agreed with coupled global model estimates (1% decline
- 		globally)
- 	-- seasonal changes variable, sometimes not significant
- 	-- comparison with in situ data indicated local overestimates
- 		in CZCS
- 	-- navigation bias removed
- 	-- blended with in situ data to remove residual biases

## Gregg and Conkright, 2002

## Antoine et al. 2005

- 	-- 25% increase, no attempt to associate with climate changes
- 	-- used new calibration methodology
- 	-- error in BRDF calculation
- 	-- No accounting for navigation errors
- 	-- seasonal changes invariable
- 	-- comparison with in situ data indicated large systematic
- 		underestimates in CZCS
- 	-- pure satellite estimate, no attempt to remove residual
- 		biases

## Antoine et al (2005)

## Global

- Expected = 0.498
- Observed = 0.498

- Expected = 0.300
- Observed = 0.294

## Slide 44

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## Slide 47