# Presentation: 054646

## Ocean Color Climate Records

-       NASA REASoN CAN

-                                  Watson Gregg
- NASA/GSFC/Global Modeling and Assimilation Office

## Ocean Color Climate Records

- Global Mean Air Temperature:
- 	0.74o increase 1906-2005 (IPCC 2007)

- From Hansen et al. 2006, PNAS

## SST: 		

- SST:
- 	 0.2oC increase 1980-2003 (OISST)

- Does ocean chlorophyll respond?
- Does ocean chlorophyll play a role?

- (from
- Rayner et al
- 192, JGR)

## Gregg et al. (2005, GRL):  4% increase 1998-2003 (P<0.05)

- 	10% increase on coasts (<200m bottom depth)
- 	No change open ocean

- Behrenfeld et al. (2006, Nature): 0.01 Tg integrated chl decrease
- 	per year 40oS to 40oN, 1999-mid-2006 (P<0.0001)
- 	No change poleward of 40o

**Global Trend Analyses**

- Both used SeaWiFS and matched changes to changes in other climate variables

## Longer-Term Global Analyses

- Gregg and Conkright (2002, GRL): 6% decline 1980’s (CZCS) to 	2000’s (SeaWiFS)
- 	Entire CZCS record (1979-1986), SeaWiFS (1997-2000)
- 	Open ocean only
- Antoine et al. (2005, JGR): 22% increase
- 	CZCS record (1979-1983), SeaWiFS (1998-2002)
- 	Case 1 waters, open ocean only; Maximum 1.5 mg m-3

- Both used consistent algorithms for CZCS and SeaWiFS

## Using a single sensor (SeaWiFS) trends can be reconciled between

- different approaches/investigators; trends are consistent with climate
- changes
- Changes determined from different sensors are not in agreement,
- despite consistent processing methodologies across sensors, but
- reconciliation is possible (confirmation is more difficult)
- MODIS-Aqua provides a test of the consistent processing/
- consistent data assumption: coincident with SeaWiFS

## 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

## Maybe there is something different between SeaWiFS and

- MODIS that is not corrected by consistent processing.
- Or maybe consistent processing is not enough.

## Ocean Color Climate Records

-       NASA REASoN CAN

- Goal:
- Provide consistent, seamless time series of Level-3 ocean color
- 	data from 1979, with a 9-year gap (1987-1996)
- 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”

## New and Post-Processing Enhancements

- Fine-tune radiance-chlorophyll relationships post-processing
- Correct for residual biases
- In situ data blending
- Integrate Models
- 	Aerosols
- 	Data assimilation
- All of the above

## 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

## Model vs. SeaWiFS:

- Bias = +5.5%
- Uncertainty = 10.1%

## 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

## 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**

## 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**

- (from Gregg and Conkright, 2002 GRL)

## 1980

- CZCS

- 1990

- 2000

- 1970

- Temperature Anomaly (oC)

- -0.2

- 0.0

- 0.2

- 0.4

- 0.6

- SeaWiFS

## CZCS Deficiencies

- Low SNR
- Solution:  Take mean over 25km
- 2) 	5 bands, only 4 of which quantitatively useful
- 		-- limits aerosol detection capability
- Solution: Innovative approaches for aerosols
- Navigation
- Solution: Bias corrected, orbit vectors obtained, reconstructing viewing angles
- 4)  El Chichon
- Solution: Tighter restriction on reflectance
- 5)  Anomalous behavior post-1981
- Solution: Don’t use Band 2
- 6)  Sampling

## CZCS Sampling

## Ship observations per decade: light symbol=10, medium=100, dark=400

- from Rayner etal 1993, JGR

## Ocean Color Climate Records

- Distinct from Operations Data Sets managed by OGBP
- Stored at GES-DAAC, access using Giovanni
- L3 format, 25-km, monthly, consistent with other climate data sets
- Includes discontinuous time series
- 	1978-1986; 	1996-2005
- 	chlorophyll only for now
- mission names not mentioned except under detailed information
- Facilitates new and post-processing advances to ensure CDR
- 	consistency
- Does not interfere with operations requirements and community

## Climate Records 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,
- 			high resolution)
- 		climate (maximum commonality/consistency of methods,
- 			low resolution)?
- 5)	How much consistency can we achieve without resorting to post-
- 		processing methods (blending of in situ data, assimilation)?