1. CLARREO Science Briefing 11/14/08 1 Reflected Solar Accuracy Science Requirements Bruce Wielicki, Dave Young, Constantine Lukashin, Langley Zhonghai Jin, Norman Loeb, Langley Peter Pilewskie, CU-LASP Kurt Thome, Steven Platnick, GSFC May 14, 2009 CLARREO Science Team Meeting May 12-15, 2009 Newport News, VA

2. CLARREO Science Briefing 11/14/08 2 CLARREO Reduces Climate Model Uncertainty Climate sensitivity is likely to be in the range of 1.5 to 4.5°C. The range of estimates arises from uncertainties in the climate models and their internal feedbacks, particularly those related to clouds and related processes. (Excerpted from IPCC) CLARREO data will be used to test the realistic range of climate predictions Reducing the range of future scenarios will enable more informed decisions concerning mitigation and adaptation

3. CLARREO Science Briefing 11/14/08 3 CLARREO / CERES Science Connections: Climate Sensitivity Uncertainty  T for 2 x CO 2 ( o C) Feedback Factor, f Current Climate Uncertainty Current measured feedback uncertainties result in large uncertainties in predicted  T (Roe and Baker, 2007).  To = the Earth’s temperature as a simple blackbody The Climate Feedback System IPCC Climate Feedback Uncertainty TotalCloudW. Vapor Lapse Rate Surface Albedo Reducing uncertainty in predictions of  T is critical for public policy since changes in global surface temperature drive changes in sea level and precipitation IPCC Mean Sensitivity The skewed tail of high climate sensitivity is inevitable in a feedback system Uncertainty in Feedback Defines Climate Sensitivity Uncertainty Feedback Factor, f The uncertainty in climate feedback is driven by these three components. The feedback for the climate system is f = 0.62 ± 0.26 (2  ) 0.26 22

4. CLARREO Science Briefing 11/14/08 What is the SW flux natural variability? 4 Monthly Tropical Average Deseasonalized Natural Variability of Reflected SW flux is 0.8 Wm -2 (1  ). This is 0.8% of the average of ~ 100 Wm -2 reflected SW flux. Global natural variability is 0.55 Wm -2 or about 0.5% of average reflected SW flux. Confirmed independently with two of the most stable instruments currently in orbit: CERES (1% accuracy), and SeaWiFS (4% accuracy but monthly lunar stability check). From Loeb et al. 2007, Journal of Climate

5. CLARREO Science Briefing 11/14/08 Variability: Time to Detect SW Flux Trend 5 Loeb et al., J. Climate 2007 IPCC anthropogenic forcing 0.6 Wm -2 /decade. 25% Low Cloud Feedback is then 0.15 Wm -2 /decade If Low Cloud Feedback Dominates in Tropics Signal = 0.3 Wm -2 /decade

6. CLARREO Science Briefing 11/14/08 Cloud Feedback Accuracy Goal 6 Why pick cloud feedback at 25% of cloud forcing? –For 3.5K doubled CO2 climate sensitivity: Clouds amplifying the forcing by 25% increases (0.75/0.6)*3.5 = 4.4K Clouds damping the forcing by 25% decreases (0.45/0.6)*3.5 = 2.6K A range of doubled CO2 of 2.6 – 4.4K greatly reduces uncertainty –If we had used 50% of forcing: range would be 1.7 to 5.2K: doesn't help: still factor of 3 uncertainty. –If we had used 10% of forcing: range would be 3.1 to 3.9K. How do we relate this to instrument gain/accuracy? –TOA SW Flux Cloud Radiative Forcing (Soden et al 2006, 2008) is the variable linearly related to cloud feedback. –TOA SW CRF = TOA SW (clear-sky) – TOA SW (all-sky) –Global Mean (CERES) TOA SW CRF = 50 – 100 = 50 Wm -2 –Accuracy for gain is then 0.15/50 = 0.3%. Require at 95% confidence.

7. CLARREO Science Briefing 11/14/08 What drives changes in global albedo? Loeb et al., 2006 J. Climate and 2007, GRL The Tropics drive global albedo variations: global is in phase with tropics and half the magnitude Cloud fraction variations are the cause, not cloud thickness

8. CLARREO Science Briefing 11/14/08 What about less well calibrated instruments like geostationary? Loeb et al., 2007 J. Climate Geo calibration & sampling errors dominate inter- annual signals Uncertainty in Geo trends are a factor of 10 larger than climate goal: calibration is critical CERES SW TOA flux in green, ISCCP FD in red

9. CLARREO Science Briefing 11/14/08 Climate Models: TOA SW vs Climate Sensitivity 9 Climate Model Sensitivity Climate Prediction.Net 2500 Perturbed Physics Runs Doubled CO2 - Normal CO2 Vary Cloud Physics 2xCO2 sensitivity 2K to 12K global temperature change. Use model output for random model pairs (I,J) to compare 2xCO2 climate change of SW TOA flux between Model I and J, with climate sensitivity difference between Model I and J. Linear relationship: 1K in sensitivity = 1.5 Wm -2 change in TOA SW flux 2xCO2 Doubled CO2 = 4 Wm -2 forcing: IPCC 0.6 Wm -2 /decade = 1/7 th or 0.2 Wm -2 for 1K sensitivity uncertainty, 0.18 for 0.9 K uncertainty. Agrees with previous 0.15 Wm -2 estimate.

10. CLARREO Science Briefing 11/14/08 10 CLARREO Science Questions: Climate Forcing Implementation Approach Key (i) Questions that CLARREO will address directly with current technology and without the need for any other observations (ii) Questions that CLARREO will address directly with expected definition study and IIP confirmation of recent advances in metrological technology and sampling strategies (iii) Questions that CLARREO will address in combination with other satellite solar and infrared sensors For each forcing, we ask 2 questions: How is this forcing changing over time (on a decadal scale)? How accurately is this forcing change represented in climate models?

11. CLARREO Science Briefing 11/14/08 11 CLARREO Science Questions: Climate Response For each response, we ask 3 questions: How is this climate response changing over time (on a decadal scale)? How accurately is this response change represented in climate model projections? What part of the change is consistent with anthropogenic forcing? Science Impact is a combination of science question importance and uniqueness of CLARREO contribution

12. CLARREO Science Briefing 11/14/08 12 CLARREO Science Questions: Climate Feedbacks and Sensitivity For each feedback, we ask 2 questions: What is the amplitude of this feedback? How accurately is this feedback change represented in climate models?

13. CLARREO Science Briefing 11/14/08 Solar Science Objectives: Key Variables 13 Solar Reflected Spectrum Forcings: –Aerosol Radiative Forcing, direct and indirect –Solar Irradiance (TSIS continuity portion of CLARREO) –Land Use Change Albedo Forcing Land surface type (e.g. IGBP surface type) Clear-sky albedo when present Solar Reflected Spectrum Feedbacks (Soden et al. 2008) –SW Broadband Cloud Radiative Forcing SW Clear-sky TOA Reflected Flux SW All-sky TOA Reflected Flux –Snow/Ice Albedo Feedback Snow/Ice cover Snow/Ice clear-sky albedo when present Solar Reflected Independent Verification of Cloud Feedback –Cloud Properties: Fraction, Visible Optical Depth, Water/Ice Phase, Particle Size

14. CLARREO Science Briefing 11/14/08 Solar Accuracy Requirements: Surface Albedo Ohring et al., 2005 BAMS: "Satellite Instrument Calibration for Measuring Climate Change", Table 2 Use that reports "stability" for decadal change as CLARREO accuracy requirement –Surface albedo: 1% (relative) of typical clear land reflectance for atmospheric window spectral regions –Cloud Optical Depth: 0.5% (relative) of typical all sky reflectance for the visible atmospheric window region (~ 0.65 micron wavelength). Scaled to be consistent with more recent Loeb et al. SW TOA flux goal of 0.15 Wm - 2 /decade for cloud feedback: this reduces it a factor of 2 from Ohring et al. –Cloud Particle Size: 0.3% (relative) for water cloud, 0.5% for ice cloud. reduced as for optical depth by a factor of 2 from Ohring et al., 2005. applies to 1600nm and 2100nm window spectral regions. –Cloud Fraction: threshold methods are relatively insensitive to calibration. Use Ohring et al cloud fraction stability (0.003/2 = 0.0015). Ackerman et al (JAOT, 2008) MODIS sensitivity to calibration error shows this requires 1.5% (relative) accuracy for MODIS bands 1 and 2 (650nm, 865nm) 14

15. Sensitivity of Earth-Reflected Solar Radiance to Water Vapor  Radiative transfer simulations used to derive changes in outgoing top-of-atmosphere spectral radiance due to 0.4 kg/m 2 per decade trend.  Largest absolute changes occur in the weak (sub-saturated) VNIR water band; largest fractional changes in the wings of the stronger SWIR bands. Clear-Sky Water Vapor: 0.3%/decade

16. Simulating CLARREO Spectra: Zhonghai Jin, LaRC Uses merged CERES/MODIS data for realistic surface, cloud, aerosol variability with latitude, longitude, sun angle. 68 months of global data for 1 degree latitude/longitude regions. Uses MODTRAN to simulate CLARREO spectra 200nm to 5000nm, with 5 cm^-1 spectral resolution Use IPCC and other estimates of potential decadal climate change to simulate spectral reflectance change per decade. Use 68 months of CERES/MODIS + MODTRAN to estimate natural variability of the spectra at monthly through annual time scales and zonal, ocean, land, global spatial scales.

17. Reflectance Change Due to Water Vapor Courtesy of Z. Jin, 2009

18. Comments  1% change over a large fraction of the spectrum  Change in sign of surface reflectance: less ice, less visible reflectance, more near-infrared reflectance.  2% change for CO 2 2  m bands.  Water vapor signal consistent with our study.  Change due to cloud has a strong surface signal. Courtesy of Z. Jin, 2009

19. CLARREO Science Briefing 11/14/08 Inter-comparison of the interannual variation of solar reflectance benchmark, the decadal climate change and the nadir sampling error. (This example is for global annual mean reflectance).

20. CLARREO Science Briefing 11/14/08 Two Paths to a Benchmark 20 Spectral Radiance (Nadir or any other viewing angle) –The primary infrared approach –The secondary solar approach –This is the most straightforward and is preferred if it has sufficient information content and sufficient sampling to avoid aliasing or sampling noise larger than climate system natural variability. CLARREO provides SI traceable calibration of other sensors: –Use where spectral radiance benchmarks lack sufficient information or sampling –CLARREO SI traceable at decadal change accuracy –Requires instruments sufficiently accurate and stable that CLARREO monthly to seasonal calibration can provide clear traceability at climate change accuracy –Requires CLARREO spectral resolution << spectral bandpass –Requires CLARREO calibrate the relevant instrument characteristics: gain, zero radiance level, polarization sensitivity to 0.5%, gain nonlinearity (~ 1% but CLARREO must characterize to ~ 0.2%), verify scan dependent characteristics