Feedbacks, Radiative Kernels and Decadal Change Observations Bruce Wielicki May 13, 2009

1. Feedbacks, Radiative Kernels and Decadal Change ObservationsBruce WielickiMay 13, 2009 CLARREO Science Team Meeting May 12-15, 2009 Newport News, VA

2. CLARREO and Decadal Climate Change • Climate Absolute Radiance and Refractivity Observatory • One of the highest priority missions described in the NRC Earth Science Decadal Survey • Recommended in first group of 4 missions (“Tier 1”) • A climate-focused mission • Foundation is on-orbit S.I. traceability of calibration • Long-term trend detection • Improvement and testing of climate predictions • Calibration of operational and research sensors • Joint NASA / NOAA mission • NOAA portion of CLARREO is the continuation of solar irradiance and earth radiation budget observations (TSIS and CERES)

3. CLARREO Reduces Climate Model Uncertainty • The range of estimates of climate sensitivity arises from uncertainties in the climate models and their internal feedbacks, particularly those related to clouds and related processes. (Excerpted from IPCC 2007) • 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

4. CLARREO’s Unique Role in Testing Climate Models But what are the observations critical to testing climate model sensitivity? 4

5. Climate Feedbacks = Radiative Kernel * Decadal Climate Change Temperature Lapse Rate and Water Vapor Feedback Example: Soden et al. J. Climate July 2008

6. Radiative Kernels for Temperature/Water Vapor Feedback: strong latitudinal dependence but weak dependence on climate model physics or radiative transfer model Water Vapor Kernel Temperature Kernel Soden et al. J. Climate July 2008

7. Using different Kernels for 14 IPCC coupled ocean-atmosphere models has little effect on determining and separating climate sensitivity factors Soden et al. J. Climate July 2008

8. All Feedbacks: Temp, W. Vapor, and Sfc Albedo are zonal land/ocean scale Cloud Feedback has much more zonal structure: ~ 1000 km. Soden et al. J. Climate July 2008

9. Cloud Feedback is from SW, LW, Net Cloud Radiative Forcing, Cloud Masking to unscramble Clear Atmosphere over Cloud. First paper to determine this correction. Soden et al. J. Climate July 2008

10. What conclusions for CLARREO? • Climate feedbacks = Radiative Kernel * Decadal Climate Change • Radiative Kernel is primarily the vertical temperature/water vapor structure in the infrared. Little vertical effect in solar (more straightforward). • Radiative Kernel is weakly dependent on radiative transfer model accuracy • CLARREO = critical decadal change of the key feedback changes: • Temperature/Lapse Rate: Temperature Profile decadal change (IR benchmark) • Water Vapor Feedback: Water Vapor Profile decadal change (IR benchmark) • Surface Albedo Feedback: MODIS/VIIRS/CERES solar clarreo calibration • Cloud Feedback: CERES IR and solar calibration (cloud radiative forcing) • Cloud masking to correct cloud radiative forcing for clear-sky effects: • IR benchmark for cloud masking, emiss*cloud fraction for masking amount, cloud pressure for masking vertical level. • Solar cloud fraction masking from calibration of MODIS/VIIRS imager • IR cloud fraction for cause/effect from calibration of MODIS/VIIRS imager Soden et al. J. Climate July 2008

11. What conclusions for CLARREO? • Zonal land/ocean resolution is sufficient for clear-sky feedbacks: • temperature, water vapor, lapse rate, surface albedo • 1000 km resolution within latitude zones is needed to capture cloud feedback spatial structure. Sampling studies underway to verify if CLARREO benchmarks can handle this directly, or if we accomplish it through CLARREO calibration of the relevant sensors: • CERES for SW, LW, Net cloud radiative forcing • MODIS/VIIRS/AVHRR imagers for cloud fraction (masking) • AIRS/IASI/CrIS for vertical cloud top layering, effective emissivity • MODIS/VIIRS/AVHRR for cloud optical depth, particle phase/size to verify cause and effect of cloud changes versus climate predictions • Surface albedo feedback in polar land regions may be sampled sufficiently by CLARREO, but if not, backup is to use CLARREO calibration of: • MODIS/VIIRS/AVHRR/CERES for snow/ice cover and albedo Soden et al. J. Climate July 2008

12. What conclusions for CLARREO? • Soden et al 2008 radiative kernel feedback paper assumes that radiative forcings are known. This means that the climate observing system needs to know the forcings in order to properly evaluate the feedbacks. • Land albedo anthropogenic forcing: only requires 1% accuracy (Ohring et al., 2005), and CLARREO will provide through calibration of MODIS/VIIRS/Landsat/CERES • Aerosol direct and indirect forcing remain a large uncertainty. • CLARREO concurs with the NRC Decadal Survey recommendation for continuity of APS beyond the GLORY mission (launch 2010) for large scale aerosol trends and improved aerosol type characterization. This is very much like the CERES/TSIS continuity part of CLARREO: but no commitment by NOAA has been made to continue APS on NPOESS. NRC NPOESS lost climate sensor report (2008) recommend further aerosol research and waiting to see how well GLORY mission APS performed. • The CLARREO and ACE teams are each evaluating the relative strengths/weaknesses of the different aerosol strategies as shown by MODIS, MISR, PARASOL, and APS. Soden et al. J. Climate July 2008

13. What conclusions for CLARREO? • Notice that there are NO cloud properties except cloud fraction used in the Soden et al analysis of cloud feedback: only LW and SW clear-sky and all-sky fluxes, temperature and water vapor profiles. When I asked Brian Soden about why: he replied that the extreme variability and nonlinearity of cloud fields has kept them from finding radiative kernels that work. • But to independently verify a climate model has the correct cloud feedback for the right reason: i.e. change in low cloud fraction (e.g. Bony et al. 2006) and not cloud optical depth: must have data on cloud properties. Example from CERES/MODIS comparisons is given on next slide showing that interannual variations at tropical mean and global mean time scale for natural variability (but NOT necessarily decadal climate change) are driven by cloud fraction change and NOT cloud optical depth or particle size (not shown but also examined and show no correlation to CERES SW flux anomalies. What conclusions for CLARREO?

14. To determine cloud feedbacks we need SW/LW Fluxes But to confirm cause/effect we need cloud properties 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 Loeb et al., 2006 J. Climate and 2007, GRL

15. What conclusions for CLARREO? • We conclude that we need TOA SW/LW fluxes (CLARREO calibration of CERES) to constrain cloud feedback in the climate system. This is already recommended in the NRC Decadal Survey. Note that if CLARREO solar and infrared are separated on different orbits, we will have to show that we can calibrate CERES Total channel in daytime (includes total solar and infrared radiation in one band) without simultaneous solar and infrared. Possible but will require demonstration and more analysis. • We conclude that we need both SW and LW cloud properties to confirm cause and effect consistency for cloud feedbacks in climate models and the Earths climate system. This requires either: • IR and solar benchmarks that can provide cloud properties on the 1000km zonal/latitude regional scales shown in the Soden et al. cloud feedback results: a sampling problem for nadir narrow swath • And/or we use CLARREO to calibrate VIIRS/CrIS/IASI to provide cloud property retrievals and use their full swath coverage to handle the spatial scale. • NRC Decadal Survey DID recommend cloud decadal change as part of its core science. It did not specifically call out for CLARREO to calibrate VIIRS/MODIS imagers in the CLARREO mission description in chapter 4. The NRC report did recommend this CLARREO calibration of VIIRS in Chapter 9.

16. Backup Slides