Introduction to CMUG assessments, SST and plans for phase 2

Roger Saunders

www.cci-cmug.org

4th Integration Meeting

CMUG Assessments of CCI CDRs

Why does CMUG assess CCI datasets?

• Provide an independent view of the datasets and associated uncertainties

• Study consistency between ECVs

• Demonstrate applications for climate modelling to accelerate use by the climate/reanalysis communities

CMUG assessments in phase 1

Methodology used

for assessment of ECVs

Assessment of precursors(see CMUG D3.1 report series)

Assessment of CCI CDRs(see CMUG D3.1 report series)

Climate Model (single, ensemble)

O3((IASI), Land Cover (GlobCover), SSH (AVISO), Cloud (ISCCP), Fire (GFEDv3)

O3, Land Cover, SM, SSH, Cloud

Re-analyses SST (HadISST), O3(ERA) O3, SM, Aerosols, GHG, SSH

Precursor datasets OC, SSH, SST, O3

Independent satellite or in situ measurements

SST (ARC) SST, O3, OC

Related observations (surface and TOA fluxes, temperature, water vapour)

Fire (GFEDv3 ) SM

Assimilation GlobColor OC

CMUG assessments of ECVs

Sea Surface Temperature

www.cci-cmug.org

Data issues found and fixed

• Data gaps– Day: western Pacific and off western coast of India (only ATSR-1

[after failure of 3.7 μm] and ATSR-2), – Night: south-western Atlantic and first 7 months of 1993.

• Abrupt change in the number of retrievals during night at ~2oS and ~8oN.

• Fill values for depth SST and uncertainties (huge numbers for 1993, 2000 and 2002 [AATSR]).

• Days without data, e.g. 29th February for the leap years.

Assessment of CCI SST

• Main validation against drifting buoys• Comparison with precursor ARC dataset

Retrieval methods– CCI: optimal estimation for ATSR-2 and AATSR, but ATSR-1 as ARC.– ARC: coefficients from radiative transfer simulations.

• Different spatial resolution– CCI: 0.05 deg– ARC: 0.1 deg

• Same cloud screening method– minor changes due to updated versions of radiative transfer and NWP

models.

Bias: ATSRs-drifting buoys

Larger bias in the tropics during day (water vapour ?) and in the mid-latitudes and over Indian and western Pacific oceans during night (systematic errors ?).

CCI ARC

night

day

Bias: Hovmoller diagrams

• AATSR performs better than ATSR-1/2.• Oscillation with a warmer bias during the boreal winter months in mid-latitudes.• Surprising difference between CCI and ARC for ATSR-1, given the same

retrieval method.

CCI ARC

night

day

3-way error analysis

• Use TMI SST for validation of ATSR-2 in the zone [-40, 40]oN for the first time.• Practically, ATSR-2 and AATSR have the same performance with standard

deviation of error 0.18 K for ARC and 0.23 K for CCI. • Drifting buoys showing an improvement of their random error with time, while

also AMSR-E has better performance than TMI.

Method which provides the random error given that the three datasets (buoys, ATSRs and MWs [TMI and AMSR-E]) are uncorrelated.

Uncertainty assessment

ARC’s uncertainty assignment is in general better than CCI’s uncertainty, although not for all uncertainty values or validation criteria.

CCI ARC

night

day

No CCI uncertainty < 0.1 K.

Conclusions on assessment of SST CCI

• 2ch biases in SST significantly higher than for ARC

• 3ch bias in SST slightly higher than ARC

• Uncertainties of CCI product suggest they are reasonable but less matchups for nightime cases than for ARC

• Feedback suggests AVHRR SST dataset is an improvement over the pathfinder SST dataset. This is good news to extend the time series back before 1995.

CMUG Phase 2

www.cci-cmug.org

4th Integration Meeting

Cross-ECV consistency

  SST SL Cl Sice OC Aero GHG LV Fire Ozone Glaci IC SM

SST   x x X X x        x  

Sea level x     x               x

Clouds x     x X x x X x X  

Sea ice X x x   X         x  x

Ocean col X    x x   x          

Aerosol     x   X     X x X x 

GHG     x      x     x X  

Landcover     x     x   x   x x

Fire     x     x x X   x   x

Ozone     x     x X        

Glaciers       x       X       x

Ice Sheets X

SoilM x x

Strong Weaker

CMUG phase 2 - Core ProposalCross-ECV dataset assessments• Cross-Assessment of marine ECVs (SST, OC, SSH, SI) (Met Office)

• Integrated assessment of the aerosol, GHG, and ozone datasets (ECMWF)

• Integrated exploitation of CCI terrestrial ECVs (LC, Fire, SM) (MPI-M/IPSL)

• Cross-Assessment of ECVs from sea-ice with atmospheric ECVs (MPI-M/DLR)

• Cross-Assessment of Aerosols, Cloud and Radiation CCI ECVs (DLR)

• Cross assessment of clouds, radn, aerosol, GHG, s moisture, SST (SMHI/MF)

Exploiting CCI products in CMIP like experiments• Assessing CCI datasets as boundary conditions in CMIP5-like atmosphere

simulations (MF/IPSL)

Adaption of community climate evaluation tools

• Benchmarking models with ESA CCI data in the era of CMIP6 (DLR/MPI-M)

• Development of community climate dataset evaluation tools (ECMWF)

Interface to climate services

Discussion After Coffee

Phase 1 Outcomes:• What additional value / problems compared to the

CMUG results were identified in climate research groups of the different ECV teams?

• What were major limiting factors in data usage identified by the CRG's?

Phase 2 and CCI-2 plans• How will phase-2 address limitations of phase-1

data identified by CMUG/CRG's ?

• What should be the new variables/products in a potential CCI-2? (e.g. Snow, Lakes, Salinity, Albedo, …….)