Traceability and Quality Issues

Bruce Wielicki NASA Langley Research Center

WIGOS Space 2040 Workshop

Geneva, Switzerland Nov 18-20, 2015

Existing TSI Composites Lack Needed Stability

Instruments chosen for composites have differing trends

10 ppm/yr stability

10 ppm/yr stability

10 ppm/yr stability

Need stability uncertainties

<10 ppm/year

Composite Choice Is Critical for Climate Researchers

Scafetta and West, Physics Today, 2008

“We estimate that the Sun could account for as much as 69% of the increase in Earth's average temperature, depending on the TSI reconstruction used.”

Global Surface Temperature anomalies Filtered Global Surface Temperatures Solar Reconstruction #1

Willson & Mordvinov, GRL, 2003 Solar Reconstruction #2

Fröhlich and Lean PMOD composite

courtesy of P. Pilewskie, LASP, Sun and Climate Symposium, Nov 2015

To Avoid Misleading

The History of Progression to Lower TSI Values

Life was pretty good...

The History of Progression to Lower TSI Values

...until SORCE launched in Jan. 2003

The History of Progression to Lower TSI Values

How socially driven is science?

Accuracy of Climate Change Observations & Predictions

Trenbrerth et al. 2013

Traceability and Quality • We currently typically rely on stability and overlap of space based

climate observations – Climate change signals are small and difficult to verify – Consistency of multiple systems all of which lack climate accuracy is weak

evidence for climate change signals. Leads to moderate not high confidence. True of most climate variable records. The classical exception: 60 year SI traceable CO2 record at Mona Loa.

• SI Traceability and accuracy in orbit must replace stability and overlap as primary source of confidence in climate observations given the large political and economic challenges

• Independent observations and independent uncertainty analysis is key to standards, traceability, and quality: analogous to metrology lab standards: but this doubles cost

• Must observe and determine evolution of climate not evolution of observing system.

• Also greatly improves interoperability of networks of systems with lower accuracy: constellations, multinational, multi source

Detection of Anthropogenic Trends • Fuzzy Lens #1: natural variability of the climate system limits even perfect

observations

• Fuzzy Lens #2: observing system uncertainty (calibration, stability, sampling, algorithm, reprocessing, data availability: including fundamental instrument calibration data)

• A climate observing system is designed to reduce the effect of observing system uncertainty to levels much less than natural variability levels (i.e. factor of 2 to 3 smaller). Beyond that is the point of diminishing returns.

• A very good metric for climate observing system accuracy is the delay in time to detect trends versus a perfect observing system (NRC Continuity, 2015): 0 yrs = 100%, 30 yrs = 0%.

• Economic value of narrowing uncertainty in future climate change by ~ 15 years is ~ $10 Trillion U.S. dollars for the world (Cooke et al. 2014, 2015).

• Tripling all global climate research investment (obs, modeling, analysis) would return $50 per $1 invested. Lose $650B per year of delay.

• Higher accuracy of trends = Much higher economic value

Critical Areas to Improve Traceability • Key advances needed in on orbit SI traceability of calibration

(SI) and retrieval algorithms (ALG) – Reflected solar narrowband radiometers and spectrometers (SI)

• Degradation in orbit due to contamination of optics/diffusers (factor of 5 to 10)

– Reflected solar broadband radiometers (0.3-3.5μm) (SI), factor of 5-10 – Spectral Solar Irradiance (SI) factor of 5 to 10 – Lunar Spectral Irradiance (SI) factor of 10 to 20 – Infrared narrowband radiometers and spectrometers (SI) factor of 5 – Broadband infrared radiometers (3.5 to 100μm) (SI) factor of 5 – Microwave passive radiometers (SI) factor of 10 – Precipitation Radar (ALG, SI) factor of 10 – Aerosols (ALG, Polarization, SI) factor of 5 – RO below 5km and above 25km (ALG) – Cloud Radar (ALG?, SI?) factor of ? TBD – Biomass SAR (ALG?, SI?) factor of ? TBD – Ice Sheet SAR dynamics: TBD

Areas with Acceptable Traceability • Lidar cloud height for top layer, and total cloud fraction. • Lidar ice sheet elevation • Sea level altimetry • Gravity for ice sheet mass • Passive total cloud fraction if cloud optical thickness > 1 and

field of view is ~ 300m or less

Use of Current Technology to Improve Traceability: available but not yet deployed

• CLARREO/TRUTHS spectrometers: full spectrum of reflected solar and infrared observations (320nm to 2300nm, 5μm to 50μm) serve as SI traceable metrology lab in orbit for 30 to 40 sensors: GSICS anchor

• Higher accuracy SSI: 0.2% (1 sigma), TSIS on ISS in 2017

• Higher accuracy Lunar spectral irradiance (CLARREO/TRUTHS)

• Use of CALIPSO backscatter lidar to monitor cloud top height/fraction: no firm observing plans past EarthCARE

• Use of wind lidar beyond ADM-Aeolus

• Multi-wavelength high spectral resolution depolarization lidar (e.g. EarthCARE, ACE) for cloud, aerosol, ocean biomass

• Multi-wavelength doppler radar for precipitation, snow

• Multi-wavelength polarization SAR: biosphere, cryosphere

• Higher accuracy metrology standards 1 to 5μm and 15 to 100μm

• More rigorous statistical analysis of potential data gaps depending on instrument and spacecraft design life, age, and launch schedules: Pgap(t)

Accuracy Requirements of the Climate Observing System

Even a perfect observing system is limited by natural variability

The length of time required to detect a climate trend caused by human activities is determined by: • Natural variability

• The magnitude of

human driven climate change

• The accuracy of the observing system

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Reflected Solar Accuracy and Climate Trends

High accuracy is critical to more rapid understanding of climate change

Climate Sensitivity Uncertainty is a factor of 4 (IPCC, 90% conf) which =factor of 16 uncertainty in climate change economic impacts Climate Sensitivity Uncertainty = Cloud Feedback Uncertainty = Low Cloud Feedback = Changes in SW CRF/decade (y-axis of figure) Higher Accuracy Observations = CLARREO reference intercal of CERES = narrowed uncertainty 15 to 20 years earlier

Wielicki et al. 2013, Bulletin of the American Meteorological Society

Calibration Reference Spectrometers (IR/RS) for Global Climate, Weather,

Land, Ocean satellite instruments Provide spectral, angle, space, and time matched orbit crossing observations for all leo and geo orbits critical to support reference intercalibration Endorsed by WMO & GSICS Calibrate Leo and Geo instruments relevant to climate sensitivity: e.g. - JPSS: VIIRS, CrIS, CERES - METOP: IASI, AVHRR - Geostationary imagers/ sounders (all) - Landsat, SPOT, MSI

Global Satellite Observations (WMO)

Global Satellite Observations (WMO)

CLARREO Instruments and Orbit • Orbit: Baseline 600 km altitude 90 degree orbit, Pathfinder tech demo: ISS

orbit (51 degree) • Reflected Solar Spectrometer:

– 320 – 2300 nm contiguous spectrum, 4 nm sampling, 8 nm resolution – 0.3% SI traceability on orbit (95% confidence) – 300 m field of view, 100 km swath – 2-axis gimbal pointing to provide intercal angle matching for full swath:

increases intercalibration sampling 100X vs SNO approach. – Verify for other instruments: offset, gain, nonlinearity, polarization

dependence, including scan angle dependence

• Infrared Interferometer – 200 to 2000 cm-1 contiguous spectrum, 0.5 cm-1 unapodized resolution – 0.07K SI traceability on orbit (95% confidence) – 25 to 100 km nadir fov, 1 sample every 200 km along the orbit track – Verify for other instruments: offset, gain, nonlinearity

• Small calibration focused instruments: ~ 50 kg, ~ 50 W

Areas in Need of Technological Development • Advances in metrology standards/methods for passive

microwave

• Advances in metrology standards/methods for lidar backscatter

• Advances in metrology standards/methods for radar backscatter

• Advances in higher vertical resolution water vapor profiles including in the boundary layer (DIAL lidar, water vapor absorption line RO)

• Advances in higher power, multifrequency, Doppler radar (e.g. precipitation and snow beyond GPM)

• Advances in higher power lidar for atmospheric composition, wind, cloud, aerosol (lidar type varies)

Areas in Need of Technological Development • Advances in defining Quantitative Earth Science Objectives (QESOs). See

the NRC Continuity 2015 report. Examples:

– Narrow the uncertainty in equlibrium climate sensitivity by a factor of 2

– Determine the acceleration of sea level rise to 1mm/yr/decade (1 sigma)

– QESOs are needed to more rigorously tie societal benefits and research goals to observing system design

• Advances in developing climate change OSSEs to better understand the relationship of QESOs to climate observing system requirements. – Weather is great at using OSSEs and OSEs, climate is not. Initial value vs

boundary condition. See NRC 2012: Quantifying uncertainty in complex models

• Advances in better understanding of the relationship of more accurate climate observations to societal economic value (e.g Cooke et al. 2014, 2015)

Summary • Traceability and Quality remain critical issues for climate

observations • Traceability and Quality would enable more effective

operational observations (interoperability, evolution) • A wide range of technological advances are ready to apply to

the challenge • A wide range of technological development could be

envisioned • The 2040 Vision forward needs to capture:

– No current designed and committed climate observing system exists – The economic value of higher accuracy climate obs is huge: ~ $10

Trillion U.S., Return on investment of $50 to $1. (3% discount rate) – Methodology advances needed to design a climate observing system:

• Quantified Science Objectives, climate OSSEs, Quality (uncertainty) requirements defined, Success Probability and Cost trades (see NRC Continuity Report, 2015)

“I skate to where the puck is going to be,

not to where it has been”

(Wayne Gretsky: when asked to explain the secret of his success)

“I skate to where the puck is going to be,

not to where it has been”

We need to skate from a Climate Observing System

toward a Climate Change Observing System

Relevant References • Cooke, R., B. A. Wielicki, D. F. Young, and M. G. Mlynczak, 2014: Value of Information for Climate Observing

Systems. Journal of Environment, Systems, and Decisions, Environ Syst Decis, 34, 98–109, DOI 10.1007/s10669-013-9451-8.

• Cooke, R., A. Golub, B. A. Wielicki, D. F. Young, M. G. Mlynczak, R. R. Baize, 2015: Real Option Value of Earth Observing Systems. Climate Policy, in press.

• Dowell, M., P. Lecomte, R. Husband, J. Schulz, T. Mohr, Y. Tahara, R. Eckman, E. Lindstrom, C. Wooldridge, S. Hilding, J.Bates, B. Ryan, J. Lafeuille, and S. Bojinski, 2013: Strategy Towards an Architecture for Climate Monitoring from Space. Pp. 39. This report is available from: www.ceos.org; www.wmo.int/sat; http://www.cgms-info.org/

• GCOS Implementation Plan, 2010. • IWG SCC (Interagency Working Group on Social Cost of Carbon, U.S. government) (2010). Social cost of

carbon for regulatory impact analysis under executive order 12866, Appendix 15a, Washington DC. http://www.epa.gov/otaq/climate/regulations/scc-tsd.pdf

• NRC (2007) Earth science and applications from space: national imperatives for the next decade and beyond. National Academies Press, Washington DC, 428 pp. ISBN-10: 0-309-14090-0

• National Research Council. 2012. Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification. Washington, D.C.: The National Academies Press.

• NRC (2015) Continuity of NASA Earth Observations from Space: A Value Framework. Release expected Fall, 2015.

• Ohring, G., B. A. Wielicki, B. Emery, R. Datla, 2005. Satellite Instrument Calibration for Measuring Global Climate Change: Report of a Workshop. Bulletin of the American Met. Soc., 1303-1313.

• Trenberth, K.E, A. Belward, O. Brown, E. Haberman, T. R. Karl, S. Running, B. Ryan, M. Tanner, and B. A. Wielicki, 2013: Challenges of a sustained climate observing system. In Climate science for serving society: Research, modeling, and prediction priorities. G.R. Asrar and J. W. Hurrell, Eds. Springer Press, 480pp.

• WCRP Grand Challenges: http://www.wcrp-climate.org/grand-challenges • Weatherhead, E.C. G.C. Reinsel, G.C. Tiao, J.E. Frederick, X.L. Meng, D. Choi, W.K. Cheang, T. Keller, J.

DeLuisi, D. Wuebbles, J. Kerr and A.J. Miller, Factors affecting the detection of trends: statistical considerations and applications to environmental data, J. Geophys. Res., 103 (D14):17,149-17,161, 1998.

• Wielicki, B.A. et al. 2013: Climate Absolute Radiance and Refractivity Observatory (CLARREO): Achieving Climate Change Absolute Accuracy in Orbit. Bull. Amer. Met. Soc., 93, 1519-1539.

Backup Slides