Improving Global Observations for Climate Change Monitoring using Global Surface Temperature (& beyond) Huai-Min Zhang & NOAAGlobalTemp Team NOAA National Centers for Environmental Information (NCEI) [formerly: National Climatic Data Center (NCDC), National Oceanographic Data Center (NODC), and National Geophysical Data Center (NGDC)] Today s Topic: Observing Systems for Climate Monitoring & Assessment Datasets & Products under NCEI Surface Oceanography unit 1 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
NOAA/NCEI Monthly Climate Monitoring & Assessment World mostly warmer but w/ cold regions Gaps in high latitudes 2 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
Foundational In-Situ Observations 3 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
Integrated In-Situ + Satellite Observations: Basic Design Principles 4 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
Climate SST Observing System Design Objective: Using OSSEs for a conscious Cost- Benefit design, implementation and operational monitoring of a global ocean observing system consisting of satellite and in-situ observations for required climate assessment accuracy, using sea surface temperature (SST) *OSSE = Observing System Simulation Experiment 5 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
I. Combined Satellite and in-situ Objective Analysis Errors Are Small Enough: Objective analysis errors on monthly and 5º scales, using AVHRR and insitu data. Even smaller with addition of TMI, AMSR-E etc. 6 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
II. An adequate in-situ network for sufficient satellite bias reduction Example of Satellite Biases - Mt. Pinatubo Eruption: SST analysis fields using an optimum interpolation (Reynolds SST) and Poisson Bias Correction Upper Panel: Without the bias correction Middle Panel: With the bias correction. Lower Panel: Removed biases using the bias correction 7 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
Typical Random Errors in Ship, Buoy and Satellite Observations Ship and Buoy: In the NOAA OI SST (Reynolds et al. 2002), assumed to be: ~ 0.5ºC for buoys ~ 1.3ºC for ships Satellite AVHRR SST: McClain et al. (1985); In the NOAA OI SST, assumed to be: ~ 0.5ºC for daytime retrievals ~ 0.3ºC for nighttime retrievals 8 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
Satellite Errors Represented by EOFs EOF Analysis: B( x, t) N 156 ai i 1 ( t)* f ( x) for monthly AVHRR SST from 1990 2002. i=1: Pinatubo i=2: Seasonal clouds & Saharan desert dust i=6: Southern Hemisphere; may be underestimated due to few in-situ data i f i (x) a i (t) i= 1 2 6 9 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009
How Dense a In-Situ Network is Needed for Satellite Bias Correction: Needed in-situ data density depends on both the magnitude and spatial patterns/scales of the biases. E.g., if the biases are the same over the global ocean, only 1 accurate in-situ data would be needed to correct the constant bias. Generally, the more complicated the bias patterns, the more in-situ data would be needed. Monte Carlo simulations are used for future biases to find the relationship between in-situ data density and satellite bias reduction. Optimal in-situ data density Current ship and buoy observations are evaluated and maps of needed buoys for a required SST accuracy are computed as a nowcast. Ship and buoy observations are combined according to their random errors
Simulation Experiments SSTs are simulated by truths + typical random noises: The truths are chosen as the climatological monthly means Simulated buoys are placed at regular grids Experiments on various grid size to find the corresponding satellite bias reduction Simulated buoy SST: Tb(x,t) = Tg(x,t) + A * e(t) Tg = ground truth (monthly climatology) A = amplitude of buoy random error = 0.5 C e(t) is a Gaussian random time series with a zero mean and standard deviation of 1 Simulated satellite data are placed at the actual monthly satellite observations Simulate monthly satellite observations (e.g., no retrievals with clouds), from January 1990 to December 2002 (t=1 to 156) Simulated satellite SST: Tsi(x,t) = Tg(x,t) + Bi(x)* a(t) i=1 to 6 for six simulated bias regimes, represented by the chosen six EOFs: Bi(x)=EOFi(x) with a global max of 2 C Tg = ground truth (monthly climatology) a(t) is a Gaussian random time series with a zero mean and a standard deviation of 1 Random noises have little effects because of large number of monthly satellite observations
2 C The max PSBE vs. buoy density (BD) on a 10 grid. Dash line is a model fit. PSBE reduces rapidly initially and then levels off with increasing buoy density. Optimal BD range is 2-5. A BD of 2 (vertical thin dash line) or more is required to reduce a 2 C bias to below 0.5 C.
Nowcast Computations EBD n b ns 7 Before Implementation: No much data in the S. Ocean After Implementation: Improved Obs in the S. Ocean
Observing System Performance Measures NOAA GPRA: Reduced Error in Global Measurement of Sea Surface Temperature (SST) GPRA SST Performance Metric Monthly Values Blue: Potential SST Error Red: Number of global drifters
Observing System Performance Measures NOAA GPRA: Reduced Error in Global Measurement of Sea Surface Temperature (SST) GPRA SST Performance Metric Monthly Values 3 years for Diagnostic & remedy
Areas for Further Development on Climate Surface Temperature Networks: Consider other SST satellites: microwave, VIIRS, etc Land Surface Temperature (LST) Observing Network Analysis Combine LST+SST and In-situ + Multiple Satellites
Some other Datasets & Products at NCEI Surface Oceanography US IOOS (Integrated Ocean Observing System) Data Archive & Stewardship SST suits of Group for High Resolution SST (GHRSST): Archive and Service Jason-2 (Ocean Surface Topography Mission (OSTM)) / Jason- 3 (altimetry): Archive & Stewardship Coral Reef Temperature Anomaly Database (CoRTAD) Satellite SAR Sea Surface Winds Product: Archive & Stewardship Satellite Sea Surface Salinity: Archive & Stewardship ICOADS: Near-real-time updates and v3 development Multi-satellite Blended Sea Surface Winds Product Global 0.25º grid, 6-hourly (updated quasi-daily), July 1987 present Near-real-time use for World Coral Watch, Ship Routing Service,
END-TO-END PROCESS: Ingest to Blended Products & Services Global Gridded Sea Surface Temperature (Reynolds OI SST) & Wind (6-hourly) Marine Surface Observations GTS N C D C NoaaPort Satellite QC; Blending Global Gridded Products http://www.ncdc.noaa.gov/oa/rsad Shown is for 6-hourly sea winds; note the simultaneous Typhoon Talim and Hurricane Katrina Interactive Data Services USERS Climate Research Decision making (observing network design & monitoring) Weather & ocean forecasts Ecosystem Marine transportation Wind/wave energy Outreach (education) NOAA Centers for Environmental Information (NCEI) 18 NOAA s National Climatic Data Center 18 27 th Sept Air-Sea/20 20101 @ th NODC Boundary Layer
Blended Surface winds: 6-hourly & ¼ global sea winds, blended from multiple (up to 6) satellites Available from July 87 onward Daily & Monthly means are also archived & served to the community Climatological monthlies were computed for base period 1995 2005 (with obs of 3 satellites) Satellite Retrievals: RSS (NASA Pathfinder) TMI SSMI SSMI QuikSCAT AMSR-E SSMI Typical sea wind speed observing satellites since June 2002 19 NOAA Centers for Environmental Information (NCEI) 19 NOAA s National Climatic Data Center 18 27 th Sept Air-Sea/20 20101 @ th NODC Boundary Layer
Sea Surface Temperature OI Blended Daily 0.25 Product (aka Reynolds SSTs) Use both AVHRR & AMSR-E since mid 2002 Great improvement in resolving frontal structures & meandering over the widely used Weekly 1 SST product Improvement is important because of largest latent heat over these Western Boundary Current regions SST Spatial Gradients 1 Weekly: AVHRR 0.25 Daily: AVHRR + AMSR-E A Fine Resolution (~ 4km) Dataset is in development 20 NOAA s National Climatic Data Center AMS 2009 Annual Meeting 16 th Conference on Air-Sea Interaction Phoenix, AZ 11-15 January 2009 NOAA Centers for Environmental Information (NCEI)
Summary We provide: Data Archive, Stewardship & Science Discovery Routine production of gridded global datasets by blending multiple-platform observations to: Increase resolution & coverage Reduce errors (both bias & analysis/sampling errors) Emphasis on: Climate Consistency (e.g. inter-sat cal. & systematic error corrections) End-to-End Process