GOES-R Support to Future Climate Monitoring Needs Mitch Goldberg Chief, Satellite Meteorology and Climatology Division Office of Research and Applications NOAA/NESDIS GOES-R Users Conference May 12, 2004

Topics Importance of climate and climate goals US Climate Change Science Program NOAA strategic plan for climate Important climate variables How GOES-R will complement NPOESS Scientific Data Stewardship

Why is Climate Important Up to 40% of nation’s $10 trillion economy affected by weather and climate Total U.S. economic impacts of the El Nino were estimated to be on the order of $25 billion US Gross Domestic Product may reduce by about 1% by 2100 due to projections of global warming

What are Major Goals in Climate? Improve predictions of climate variability and change –ENSO forecasts –Global sea level –Decadal climate forecasts Improve predictions of recovery of the stratospheric ozone layer Improve predictions of CO 2 out to 100 years Develop credible ecological forecasts due to global climate change

What are Major Goals in Climate? (cont.) How will water cycle dynamics change in the future? –Improve seasonal forecasts of precipitation –Improve long range water cycle prediction for planning energy needs Better understand and quantify the role of aerosols.

US Climate Change Science Program (CCSP) Vision: A nation and the global community empowered with the science-based knowledge to manage the risks and opportunities of change in the climate and related environmental systems

CCSP Goals Improve knowledge of the Earth’s past and present climate and understanding of the causes of observed variability and change Improve quantification of forces that control climate Reduce uncertainty in climate projections Understand sensitivity and adaptability of ecosystems and humans to climate Explore uses and identify limits of knowledge to manage risks and opportunities of climate variability and change

CCSP Core Approaches Scientific research Observations Decision support Communications

NOAA Strategic Plan: Climate Build an end-to-end system of integrated global observations of key atmospheric, oceanic, and terrestrial variables Enhance scientific understanding of past climate variations and present atmospheric, oceanic, and land–surface processes that influence climate Apply this improved understanding to create more reliable climate predictions on all time scales Establish service delivery methods that continuously assess and respond to user needs with the most recent, reliable information possible.

Climate Variables and their Importance Forcing: external variables that control climate Response: variables that respond to climate forcing Feedback: variables that respond to climate forcing and modify the forcing

Climate Variables and their Importance (cont.) Forcing: solar irradiance, CO 2, CH 4, O 3, N 2 O, aerosols Response: temperature, winds, precipitation, sea level Feedback: water vapor, clouds, snow/ice cover, vegetation, ocean color, earth radiation budget Sea Level Rise (Cheney, 2004)

Indicators of a warming climate Increase in temperature, decrease in the diurnal temperature range More intense precipitation events Increase of summer droughts Increase in tropical cyclone intensities Intensified droughts and floods associated with El Nino Increase of sea level

GOES-R High Quality Observations will Complement NPOESS by : Resolving the diurnal cycle and its long-term changes –Diurnal cycle and the seasonal cycle are the two largest climatic variations –Variables with large diurnal variations include hydrological variables, ERB, and surface temperature Providing more opportunities each day to obtain visible and infrared observations that are not degraded by cloud cover Monitoring rapidly changing and rare climate phenomena (e.g., precipitation occurs only about 20% of the time) Serving as a calibration anchor for all NPOESS satellites

Current and Past GOES Satellites are Major Contributors to World Climate Research Program’s: International Satellite Cloud Climatology Project Global Precipitation Climatology Project Surface Radiation Budget Climatology Project

detecting change understanding processes understanding change stability uncertainty Attribution low high lowhigh Desired characteristics of an observing system (After G. Stephens, 2003)

Scientific Data Stewardship for Climate The goal is to ensure that satellite observations and products are processed and used in a manner that is scientifically defensible, not only for real-time assessments and predictions of climate, but for retrospective analyses, re- analyses, and reprocessing efforts. Primary functions include: –Careful monitoring of observing system performance –Generation of climate data records CDRs provide information to: – monitor change (climate variability and trends) of the Earth’s climate. – predict change – especially SI forecasts – input to model re-analyses (note: reanalysis is also a CDR) – validate climate prediction models and model reanalyses – understand processes ( water vapor-cloud-radiation feedback) In preparation of this new program, NESDIS is developing a plan for generating CDRs from NOAA operational satellites, which will be reviewed by the National Academies.

Academy Study National Academy interim report to NESDIS providing general guidance on satellite- based CDR program. Overarching recommendation: NOAA to develop CDR program, apply new approaches to generate and manage satellite CDRs, develop new community relationships and ensure long- term consistency and continuity for a satellite CDR program Program plan from NESDIS due 8/04, and will be reviewed by Academy (after approval by senior NESDIS mgt)

What is a CDR? A time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change Fundamental CDRs – calibrated and quality- controlled sensor data that have been improved over time. Thematic CDRs – geophysical parameters derived from the FCDRs.

Creating Quality Climate Data Records Requires: –Lowest level of data (level 1) be preserved with complete documentation and metadata, includes data that records the satellite and instrument performance –Observing performance monitoring to minimize spatial and temporal biases Tools to detect and account for changes in the observing system –Science team guidance and participation –Near Real-Time CDR Generation Tight connection between algorithm developer and CDR generator (may be same group) Strong calibration/validation program Research with the data set as part of the program Collaboration with user communities (e.g., diagnosticians, modelers) to obtain feedback

Creating Quality Climate Data Records Requires (cont): Re-processing –An improved algorithm is developed –New information on an instrument –An error is discovered in the processing system Research and Application –Development of climate quality algorithms –Analysis of time series to detect emerging trends –Joint studies with climate modeling community –Production of periodic assessments for decision makers, other climate researchers and the public Data Requirements –End-to-end data management –Near real-time access to data (including raw radiances) –Development of community consensus algorithms and data standards –Complete archiving: data, meta data, source code, ancillary data, etc. –Free and open sharing and exchange of climate data »Nationally and internationally

–Observing system performance monitoring Detect problems early –Production of near real-time CDRs Monitor current state of climate system and short -term variations –Reprocessing of CDRs for long-term records Consistent, seamless, high quality time series with minimized bias –Climate research and applications Joint activities with external community –Archive and distribution Includes output of above activities, metadata, and timely distribution Functional Areas for CDR System

Summary GOES-R will have a major contribution to monitoring, understanding and predicting climate change and variability. –Monitor and understand diurnal changes –Intercalibrate polar-orbitting satellites Long-term stability of the sensors is very important Production of climate data records are different from environmental data records, so a program supporting the development of CDRs is needed.

Vision A climate science community empowered with the high quality satellite-based climate data records needed to define global climate variations and change

Backup slides

Stability is a property of time variation of uncertainty (and hence accuracy and precision) time uncertainty stable measurement unstable measurement Unstable measurement, uncertainty grows as a function of time. This growth can occur through growth in accuracy error and growth in precision error. Time, t 1 Time, t 2 Traits: Stability & Bias (After Stephens, 2003)

True y accuracy, a Measured y precision, p Uncertainty, u =  a 2 +p 2 Traits: Accuracy, precision and Uncertainty (After Stephens, 2003)

Traits: Stability & bias (After Stephens, 2003)