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Catia Domingues1, on behalf of
the CLIVAR/GSOP workshop team and international partners
IQuOD Interna,onal Quality-‐Controlled
Ocean Database (a proposal in development)
CASS, Sydney, December 2013
1ACE CRC, Hobart
Earth climate variability and change: ocean’s role
AR4: Bindoff et al. (2007) AR5: Rhein et al. (2013)
Ocean heat storage > 90%
Ocean:
-‐ 70% earth’ surface -‐ average depth: 3,800 m.
Ocean temperature/salinity
observa*ons are essen*al to the
understanding of variability and
change in the Earth's energy and
water cycle, and to discriminate
between natural and
anthropogenic drivers,
par*cularly now in the context of
climate change.
Earth’s total heat content
Interna,onal Argo profiling floats array (since mid 2000s)
• 3,000 floats active (3x3 deg array design) • 10-day profiling cycle / satellite transmission • 3-5 year life cycle • Data publically available (web US/mirrors) • Quality: real-time/delayed mode • Tracking impact (e.g., science / citations )
Challenges: • To sustain optimal no. floats • To expand array (e.g., deep ocean (> 2,000 m),
marginal seas, under sea-ice) • To include other sensors (e.g., oxygen, biogeochemistry)
Iden,fica,on firmware problems: Argo (SOLO FSI) floats
“Recent cooling” (Lyman et al. 2007)
Correction: Willis et al. 2007 (firmware problem in SOLO FSI floats)
Ocean heat content change
Year
Corrected
Time-evolution of surface pressure drifts (dbar)
Barker et al. (2011)
Pressure biases in dominant (APEX) type of Argo floats
Argo program is delivering pressure-‐adjusted APEX profiles, consitent technical informa,on, and flagging uncorrectable float data. 2002 2005 2008
Public as of Jan 2009
Pressure correctable
Impact on global thermosteric sea level (mm)
Warm biases
Mixed biases
700 m
0 m
0 m
700 m
Before Argo floats array …
Argo float ( autonomous CTD
“robot” )
Historically subsurface observing system not designed for climate monitoring. Mix of instruments (varying precisions/biases).
Bottle & CTD (OSD) ( most accurate & expensive )
MBT & XBT ( 70% historical data base )
Historical ocean database
Courtesy: Tim Boyer, NODC NOAA
Instrumental (,me-‐dependent) biases – MBTs/XBTs
Closer scrutinity: Gouretski and Koltermann (2007)
Wijffels et al. (2008) Domingues et al. (2008)
Implications for ocean warming variability and trend
Key uncertainty in IPCC AR4 report (Solomon et al., 2007)
Several proposed XBT bias correc,ons
FAQ: Which to use?
Abraham et al. (2013)
Missing XBT metadata
Abraham et al. (2013)
Fraction Unknown type
Substandard data quality
Gronell and Wijffels (2008)
Gaussian Tail for positive
depth error is much bigger
Automated QC only designed to detect obvious errors. It can accept bad data as good and incorrectly iden,fy good data as bad. Expert Manual QC: found 16% of bad data (some within background sta*s*cs).
If same % bad data maintained for world ocean : ∼1.5 million BAD temperature profiles
Implica,ons: a rela*vely large amount of non-‐trivial errors in the data can lead to rapid and/or slowly-‐varying ar*facts in the evolu*on of ocean climate signals at global and regional scales.
Pilot study: Indian/Southwest Pacific Ocean
Courtesy: Josh Willis, JPL NASA
IQuOD – Project Proposal
http://www.clivar.org/organization/gsop/activities/clivar-gsop-coordinated-quality-control-global-subsurface-ocean-climate
1st workshop: Hobart June 2013 2nd workshop: WashingtonDC June 2014 (GTSPP/Belgium)
Interna,onal Quality-‐controlled Ocean Database
Project Proposal • An interna,onally-‐coordinated approach to quality control ocean
temperature (>13 million profiles) and salinity (in the near future) data. • Aim is to maximize the availability and consistency of these valuable and
irreplaceable historical ocean subsurface data and to include proper characteriza,on of uncertainty.
IQuOD – Project Proposal
Interna,onal Quality-‐controlled Ocean Database
The World Ocean Database (WOD) ideal star,ng database ( >18,000 datasets consolidated in one format) Huge QC challenge due to numerous sources/instrument (accuracies/biases) contribu*on over the years. First IQuOD phase is to focus on the period pre-‐1990. Coordinated and well established QC methods (e.g., Argo QC) will be applied and the resul*ng QC evaluated. WOD will also be the reassembly point, storing and pos*ng final data and QC flags for IQuOD public dissemina*on.
An agreed upon standard set of automated QC procedures will be developed to be quickly applied to any temperature profile type (currently groups are applying their automa*c procedures to test datasets of known quality for comparison and assessment). Automa,c QC will iden,fy the frac,on of profiles which requires addi,onal manual expert review (in the case of current WOD QC, this is ~7.5% of the temp. profiles and in the case of QuOTA (Gronell and Wijffels, 2008), ~32%.
Each suspect temperature profile will be manually examined and final QC decisions made. Centers of exper,se will be designated based on ins*tu*onal knowledge of the temperature structure of geographic regions, familiarity with instrumenta,on and QC challenges of historical observa,onal periods (e.g., wire angle depth errors in boele casts pre-‐World War II). Centers of exper*se will communicate and compare QC decisions to reach an equilibrium between standardiza,on and specialized knowledge. Tools similar to Mquest (Gronell and Wijffels, 2008, J. At. Oc. Tech.) will be shared to facilitate manual QC.
1. Global Data Assembly Center
The World Ocean Database (WOD) ideal star,ng database ( >18,000 datasets consolidated in one format) Huge quality control challenge due to numerous data sources/instrument types (biases) over the years. First IQuOD phase is to focus on the period pre-‐1990. Coordinated and well established QC methods (e.g., Argo QC) will be applied and the resul*ng QC evaluated. WOD will also be the reassembly point, storing and pos*ng final data and QC flags for IQuOD public dissemina*on.
IQuOD – Project Proposal
Interna,onal Quality-‐controlled Ocean Database
The World Ocean Database (WOD) ideal star,ng database ( >18,000 datasets consolidated in one format) Huge QC challenge due to numerous sources/instrument (accuracies/biases) contribu*on over the years. First IQuOD phase is to focus on the period pre-‐1990. Coordinated and well established QC methods (e.g., Argo QC) will be applied and the resul*ng QC evaluated. WOD will also be the reassembly point, storing and pos*ng final data and QC flags for IQuOD public dissemina*on.
An agreed upon standard set of automated QC procedures will be developed to be quickly applied to any temperature profile type (currently groups are applying their automa*c procedures to test datasets of known quality for comparison and assessment). Automa,c QC will iden,fy the frac,on of profiles which requires addi,onal manual expert review (in the case of current WOD QC, this is ~7.5% of the temp. profiles and in the case of QuOTA (Gronell and Wijffels, 2008), ~32%.
Each suspect temperature profile will be manually examined and final QC decisions made. Centers of exper,se will be designated based on ins*tu*onal knowledge of the temperature structure of geographic regions, familiarity with instrumenta,on and QC challenges of historical observa,onal periods (e.g., wire angle depth errors in boele casts pre-‐World War II). Centers of exper*se will communicate and compare QC decisions to reach an equilibrium between standardiza,on and specialized knowledge. Tools similar to Mquest (Gronell and Wijffels, 2008, J. At. Oc. Tech.) will be shared to facilitate manual QC.
2. Automa,c Quality Control An agreed upon standard set of automated QC procedures will be developed to be quickly applied to any temperature profile type (currently groups are applying their AQC procedures to test datasets of known quality for comparison and assessment). Automa,c QC will iden,fy the frac,on of profiles which requires addi,onal manual expert review (in the case of current WOD QC, this is ~7.5% of the temp. profiles and in the case of QuOTA (Gronell and Wijffels, 2008), ~32%.
IQuOD – Project Proposal
Interna,onal Quality-‐controlled Ocean Database
The World Ocean Database (WOD) ideal star,ng database ( >18,000 datasets consolidated in one format) Huge QC challenge due to numerous sources/instrument (accuracies/biases) contribu*on over the years. First IQuOD phase is to focus on the period pre-‐1990. Coordinated and well established QC methods (e.g., Argo QC) will be applied and the resul*ng QC evaluated. WOD will also be the reassembly point, storing and pos*ng final data and QC flags for IQuOD public dissemina*on.
An agreed upon standard set of automated QC procedures will be developed to be quickly applied to any temperature profile type (currently groups are applying their automa*c procedures to test datasets of known quality for comparison and assessment). Automa,c QC will iden,fy the frac,on of profiles which requires addi,onal manual expert review (in the case of current WOD QC, this is ~7.5% of the temp. profiles and in the case of QuOTA (Gronell and Wijffels, 2008), ~32%.
Each suspect temperature profile will be manually examined and final QC decisions made. Centers of exper,se will be designated based on ins*tu*onal knowledge of the temperature structure of geographic regions, familiarity with instrumenta,on and QC challenges of historical observa,onal periods (e.g., wire angle depth errors in boele casts pre-‐World War II). Centers of exper*se will communicate and compare QC decisions to reach an equilibrium between standardiza,on and specialized knowledge. Tools similar to Mquest (Gronell and Wijffels, 2008, J. At. Oc. Tech.) will be shared to facilitate manual QC.
3. Manual Quality Control Each suspect temperature profile will be manually examined and final QC decisions made. Centers of exper,se will be designated based on ins*tu*onal knowledge of the temperature structure of geographic regions, familiarity with instrumenta,on and QC challenges of historical observa,onal periods (e.g., wire angle depth errors in boele casts pre-‐World War II). Centers of exper*se will communicate and compare QC decisions to reach an equilibrium between standardiza,on and specialized knowledge. Tools similar to Mquest will be shared to facilitate manual QC. (Gronell and Wijffels, 2008)
IQuOD – Project Proposal
Interna,onal Quality-‐controlled Ocean Database
The World Ocean Database (WOD) ideal star,ng database ( >18,000 datasets consolidated in one format) Huge QC challenge due to numerous sources/instrument (accuracies/biases) contribu*on over the years. First IQuOD phase is to focus on the period pre-‐1990. Coordinated and well established QC methods (e.g., Argo QC) will be applied and the resul*ng QC evaluated. WOD will also be the reassembly point, storing and pos*ng final data and QC flags for IQuOD public dissemina*on.
An agreed upon standard set of automated QC procedures will be developed to be quickly applied to any temperature profile type (currently groups are applying their automa*c procedures to test datasets of known quality for comparison and assessment). Automa,c QC will iden,fy the frac,on of profiles which requires addi,onal manual expert review (in the case of current WOD QC, this is ~7.5% of the temp. profiles and in the case of QuOTA (Gronell and Wijffels, 2008), ~32%.
Each suspect temperature profile will be manually examined and final QC decisions made. Centers of exper,se will be designated based on ins*tu*onal knowledge of the temperature structure of geographic regions, familiarity with instrumenta,on and QC challenges of historical observa,onal periods (e.g., wire angle depth errors in boele casts pre-‐World War II). Centers of exper*se will communicate and compare QC decisions to reach an equilibrium between standardiza,on and specialized knowledge. Tools similar to Mquest (Gronell and Wijffels, 2008, J. At. Oc. Tech.) will be shared to facilitate manual QC.
4. Delivery of uniform quality-‐controlled database with uncertainty es,mates.
(including provision of
“intelligent metadata”)
IQuOD – Project Proposal
IQuoD Proposal – Summary
The World Ocean Database (WOD) ideal star,ng database ( >18,000 datasets consolidated in one format) Huge QC challenge due to numerous sources/instrument (accuracies/biases) contribu*on over the years. First IQuOD phase is to focus on the period pre-‐1990. Coordinated and well established QC methods (e.g., Argo QC) will be applied and the resul*ng QC evaluated. WOD will also be the reassembly point, storing and pos*ng final data and QC flags for IQuOD public dissemina*on.
An agreed upon standard set of automated QC procedures will be developed to be quickly applied to any temperature profile type (currently groups are applying their automa*c procedures to test datasets of known quality for comparison and assessment). Automa,c QC will iden,fy the frac,on of profiles which requires addi,onal manual expert review (in the case of current WOD QC, this is ~7.5% of the temp. profiles and in the case of QuOTA (Gronell and Wijffels, 2008), ~32%.
Each suspect temperature profile will be manually examined and final QC decisions made. Centers of exper,se will be designated based on ins*tu*onal knowledge of the temperature structure of geographic regions, familiarity with instrumenta,on and QC challenges of historical observa,onal periods (e.g., wire angle depth errors in boele casts pre-‐World War II). Centers of exper*se will communicate and compare QC decisions to reach an equilibrium between standardiza,on and specialized knowledge. Tools similar to Mquest (Gronell and Wijffels, 2008, J. At. Oc. Tech.) will be shared to facilitate manual QC.
Aim: Improve quality standard/volume data/metadata historical ocean database; include error measurements; via a globally-‐coordinated approach/coopera*on (no single group has the manpower/resources/exper*se to do the whole job). Depend on different people skills (scien*sts/technical/managers). For example: (i) QC experts (e.g., manpower). Unified QC system (same method/protocols/training). Relying on relevant feedback from experienced QC experts that can be incorporated into the unified QC system during its development phase. (ii) Project leaders/managers (e.g., to find funding support to carry out project) at country level. (iii) People that might be able to help us access historical/modern original data/metadata which are sikng somewhere but is not yet publically available (e.g., Navy data?)
( still very much in “getting started” mode )
Earth climate variability and change: ocean’s role Ocean:
-‐ 70% earth’ surface -‐ average depth: 3,800 m.
Ocean temperature/salinity
observa*ons are essen*al to the
understanding of variability and
change in the Earth's energy and
water cycle, and to discriminate
between natural and
anthropogenic drivers,
par*cularly now in the context of
climate change.
“Cost”
Ocean warming
Volume expansion
Thermosteric sea-‐level rise
A major component of 20th century sea level rise, will con*nue to be in the 21st century and beyond (eg. large thermal iner*a).
Church et al. (2013)
