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Chris MerchantThe University of Edinburgh

The SST CCI:Scientific Approaches

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OUTLINEThe SST CCI: Scientific Approaches

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• What are we aiming for in a satellite SST CDR?

• What do current techniques give?

• What will we try in SST CCI?

• External involvement in SST CCI

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WHAT ARE WE AIMING FOR?The SST CCI: scientific approaches

Click to edit Master title styleRequirements for SST CDRProperty GCOS (2006)

statementCCI survey 2010

Accuracy 0.25 K 0.1 K on 100 km scales

Stability 0.1 K / decade 0.1 K / decade

Random uncertainty -- 0.1 K

Spatial resolution 1 km 0.1o (<1 km)

Temporal resolution 3 hourly Daily (3 hourly)

Uncertainty information -- Total uncertainty in every cell. Error covariance information.

Quality information -- Simple: probability of “bad”

SST meaning -- Skin and depth required

Independence -- Preferred by 60%

Click to edit Master title styleIndependence

• Two meanings of independence

– Retrievals not tied to in situ observations

– Information for SST in retrieval near 100%

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∂ˆ x ∂x ≈1

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WHAT DO CURRENT TECHNIQUES GIVE?

The SST CCI: scientific approaches

Click to edit Master title stylePathfinder v5 NLSST

1 year Metop-A >200000 drifter night-time matchesSingle pixel Located at buoy MAD time 1h20

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Map

Derive coefficients and bias

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ˆ x − x

MDSSTs, x

BTs, yLeast squares regression

Coefficients, a

Predicted SST, ,given y and ax̂

Click to edit Master title styleRegional annual biases

Click to edit Master title style“Random” uncertainty

Click to edit Master title styleDependence on prior

Algorithm

Sensitivity to true SST, x

1232113210ˆ yxaSayxaSaaax bb

x

yxaSa

x

yxaSaa

x

xbb

12

3211

321

ˆ

Fraction of information from prior

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1 −∂ˆ x

∂x

Click to edit Master title styleImperfect sensitivity to SST

Change in NLSST for a 1 K change in SST

Click to edit Master title styleStability

• Zero mean bias against drifting buoy sample

• Prior error depends on mean of matches

• Stability could depend on buoy distribution

• Needs to be assessed€

ˆ x − x = aTK − i( ) x − x( )

Click to edit Master title styleIssues with NLSST for CDR

• Empirically tied to drifting buoys– Neither skin nor depth SST– Not independent– Dependence of bias on evolving match-up?

• Biases and “random” errors exceed user requirements

• Dependence: (5% to 60%) of result supplied by implicit prior

Click to edit Master title styleHow to improve on NLSST?

• Use 3.7 um when available– Improves on bias, precision and prior dependence– But introduces day-night inconsistencies

• Banding of coefficients– Latitude, TCWV

• Bias correction by simulation– Le Borgne, 2011, doi:10.1016/j.rse.2010.08.004

• Optimal estimation

Click to edit Master title styleATSR Reprocessing for Climate

• >15 years global coverage, 0.1 deg• Accuracy < 0.1 K• Stability of 0.05 K per decade• Both skin and depth SSTs• Diurnal cycle removed• Comprehensive error characterization• Independent of other records

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18

Radiative transfermodeling and inverse theory

Physical modelsof skin and

stratification

Probabilistic,physically

based

Click to edit Master title styleARC SST mean v. drifters

(a) N2 (b) N3(c) D2 (d) D3

Click to edit Master title styleARC SST RSD v. drifters

Click to edit Master title styleARC dependence on prior

(a) N2 (b) N3(c) D2 (d) D3

Click to edit Master title styleARC stability (provisional)

Global oceans (data gaps filled)Provisional homogeneity ATSR2/AATSRTrend uncertainty magnitude displayed relative to end of time-series

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WHAT WILL WE TRY NEXT?The SST CCI: scientific approaches

Click to edit Master title styleBringing AVHRR and ATSR together

Tie AVHRR to ATSR instead of buoys– Basis for independence, traceable to physics of radiative

transfer

Not merely adjusting AVHRR SST bias to ATSR

Use common Optimal Estimation retrieval for IR– Overcome information deficit in single view– Meet 0.1 K bias target– Information content / prior dependence known

Click to edit Master title style(Sub) System for Long-term CCI SST

Development logic for AVHRRoptimal estimateretrieval (“OE2”)

Multi-sensor match-up data set

Mean diurnal cycle

AVHRR orbit drift

AVHRR orbit drift

Characteristics of Long Term CCI SSTPATHFINDER

ARC CCI SST

Sensors AVHRR ATSR AVHRR + ATSR

Tied to Drifting buoys

Independent Independent

Homogenized No Yes Yes

Accounting for diurnal effects No Yes Yes

Meets GCOS accuracy (0.25 K) No Yes Yes

Meets ARC target accuracy (0.1 K) No Mostly Yes/mostly

Retrieval method Coefficients Coefficients Optimal

Meets GCOS stability ? Likely Likely

Stability quantified No Yes Yes

Clearly defined SST No SST-skin & depth SST-skin & depth

Stable during strat. aerosol No Yes Yes

Quantified uncertainties No Yes Yes

Spatial resolution 4 km 0.1o 1 km to 0.05o

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EXTERNAL INVOLVEMENT IN SST CCIThe SST CCI: scientific approaches

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Augment Multi-sensor Match-up Dataset– Talk to us now!

Algorithm selection round robin– August 2011 to November 2011

Climate Data Research Package– January 2013

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THANK YOU FOR YOUR ATTENTION.QUESTIONS?

The SST CCI: scientific approaches