1. Climate Model Observing System Simulation Experiments Bill Collins UC Berkeley and LBL with A. Lacis and V. Ramaswamy

2. Topics Motivation for climate modeling applications Goals of the observing system simulation Major components of the OSSE Proposed emulators Description of the OSSE

3. Reductions in Arctic sea ice Arctic summer sea ice extent is shrinking at 7.4+2.4% per decade. IPCC AR4, 2007 NASA & NSIDC

4. Further reductions in Arctic sea ice 2000 2100 IPCC AR4, 2007

5. Trends in N. hemisphere snow cover Since 1988, snow cover has declined by 5%. Linear trend is -0.9+0.4% per decade. IPCC AR4, 2007

6. Projections for snow cover: 2000 to 2100 IPCC AR4, 2007 Supplementary Figure S10.1. Multi model mean snow cover and projected changes over the 21st century from 12 (a and b) and 11 (c) AOGCMs, respectively. a) Contours mark the locations where the December to February (DJF) snow area fraction exceeds 50%, blue for the period 1980–1999, and red for 2080–2099, dashed for the individual models and solid for the multi model mean. b) Projected multi model mean change in snow area fraction over the period 2080–2099, relative to 1980-1999. Shading denotes regions where the ensemble mean divided by the ensemble standard deviation exceeds 1.0 (in magnitude), Supplementary Figure S10.1. Multi model mean snow cover and projected changes over the 21st century from 12 (a and b) and 11 (c) AOGCMs, respectively. a) Contours mark the locations where the December to February (DJF) snow area fraction exceeds 50%, blue for the period 1980–1999, and red for 2080–2099, dashed for the individual models and solid for the multi model mean. b) Projected multi model mean change in snow area fraction over the period 2080–2099, relative to 1980-1999. Shading denotes regions where the ensemble mean divided by the ensemble standard deviation exceeds 1.0 (in magnitude), Snow CoverSnow Cover Change

7. Low confidence in cloud evolution IPCC AR4, 2007 Change in cloud amount in 21st century: A1B Scenario

8. Uncertain cloud radiative response Models do not converge on sign of change in cloud radiative effects. Trends in cloud radiative effects have magnitude < 0.2 Wm -2 decade -1. Change from 1980-1999 to 2080-2099 Change in cloud radiative effects in 21st century: A1B Scenario IPCC AR4, 2007

9. Low confidence in cloud feedbacks IPCC AR4, 2007 Change in cloud radiative effects: 1% CO 2 /year simulations

10. Goals of the OSSEs Test the detection and attribution of radiative forcings and feedbacks from the CLARREO data: Determine feasibility of separating changes in clouds from changes in the rest of the climate system In solar wavelengths, examine feasibility of isolating forcings and feedbacks Quantify the improvement in detection and attribution skill relative to existing instruments

11. Role of climate models in OSSEs Goals of OSSEs require projections of climate change. Sole source of these projections: climate models Advantages of climate models for this application: Identification of forcings for each radiatively active species Separation of feedbacks associated with water vapor, lapse rate, clouds Tests of CLARREO concept with climate models To what extent can forcings and feedbacks can be separated and quantified using simulated CLARREO data? What are the time scales for unambiguous detection and attribution?

12. Schematic of Tests Forcing Climate Models CLARREO Emulator CLARREO Emulator CLARREO Forcing Compare Forcing Climate Models CLARREO Emulator CLARREO Emulator CLARREO Feedback Compare Model Feedback

13. Individual forcings in Climate Models IPCC AR4, 2007MIROC+SPRINTARS

14. Individual feedbacks in Climate Models IPCC AR4, 2007

15. Major steps in Climate OSSEs 1.Conduct OSSEs with 3 models analyzed in the IPCC AR4 2.Add adding two new components to these models : A.Emulators for the shortwave and infrared CLARREO B.More advanced spectrally resolved treatments of surface spectral albedos 3.Results from emulators serve as surrogate CLARREO data 4.Estimate the forcings and feedbacks from emulators 5.Compare to forcings / feedbacks calculated directly from model physics

16. Models for Climate OSSEs Three models for OSSEs: NASA Goddard Institute for Space Studies (GISS) modelE (Schmidt et al, 2006) NOAA Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model CM-2 and CM-2.1 (Delworth et al, 2006) NCAR Community Climate System Model CCSM3 (Collins et al, 2006).

17. Model Simulations for Climate OSSEs Three classes of simulations for OSSEs: Pre-industrial conditions with constant atmospheric composition 21 st century with the IPCC emissions scenarios 20 th and/or 21 st centuries with single forcings, e.g., just CO 2 (t) IPCC AR4, 2007

18. Candidate CLARREO Emulator MODerate spectral resolution atmospheric TRANSmittance (Modtran4) version 3 (Berk et al, 1999) Spectral resolution of Modtran4: 0 to 50,000 cm -1: 1 cm -1 Blue and UV:15 cm -1 Relationship to CLARREO: Infrared : 1X UV/Blue/NIR:10-100X Alternate emulators: GISS, GFDL, and NCAR LBL codes Berk et al, 1999

19. Features of Modtran4 Modtran4 includes: Correlated-k treatment of atmospheric transmission BDRFs for non-Lambertian surfaces Line parameters obtained from Hitran 2002 database Berk et al, 1999

20. Advantages of Modtran4 Economical compromise among resolution, accuracy, and speed Team members have experience using Modtran to simulate AIRS Modtran is a community-standard radiative transfer code Huang et al, 2007

21. Timing of Modtran4 CPU time for IR calculations: Resolution: 1 cm -1 Range: 100-3333.0 cm -1 CPU time for IR calculations: Resolution:15 cm -1 Range:3333.0-33333.0 cm -1 Calculation specs: 25-level standard cloud-free tropical profile CPU = Intel Dual-core 1862.166 MHz processor Implications: ~Few hours CPU time per simulated month Total (s)User (s)System (s)Utilization 0.730.460.0164% Total (s)User (s)System (s)Utilization 1.160.910.0179%

22. Primary steps in the OSSE Phases for the study: Linking the CLARREO emulator Modtran4 with the climate models Adoption of spectral surface emissivity and BDRF models Simulations for a constant composition to determine the natural variability Simulations of CLARREO measurements for transient climate change Model Archive Model Archive CLARREO Emulator CLARREO Emulator Emulation Validation

23. Natural variability in the spectra Huang et al, 2002 25-day Variability, Central Pacific 25-day Variability, Western Pacific Goal: quantify signal-to-noise ratios for forcings and feedbacks (cf Leroy et al, 2007).Calculations: pre-industrial conditions for “background” radiance field Goal: quantify signal-to-noise ratios for forcings and feedbacks (cf Leroy et al, 2007).Calculations: pre-industrial conditions for “background” radiance field

24. Issues for the Emulation For speed and expediency, we recommend using using the existing IPCC AR4 archive for emulation. The reason? Centennial length simulations are very expensive. The trade-offs: Highest temporal sampling: daily means of model state Nominal temporal sampling: monthly means of model state This precludes reproducing the space-time track of CLARREO’s orbit For solar, we can reproduce monthly-mean solar_zenith (latitude) Result: Our results are an upper bound on detection/attribution skill Our results would reflect perfect diurnal sampling at each model grid point. Alternate, but very remote, possibility: “time-slice” experiments Advantage: interactive coupling and capture space-time sampling 19501960197019801990200020102020203020402050 Time Slice

25. Issues for the Emulation, part 2 Atmospheric conditions: All-sky: predominant condition for 100-km pixels Clear-sky: sets upper bound for detection-attribution skill for non-cloud forcings and feedbacks Detection and attribution: projection onto spectral “basis functions” for single forcings and feedbacks Anderson et al, 2007

26. First Six Months Objective: Configuration and initiation of the OSSEs Acquisition of licenses and software for Modtran 4, the CLARREO simulator Development of interfaces between IPCC models and Modtran 4 Automation of software for analysis of IPCC simulations with Modtran 4 Introduction of spectral surface emissivity and bi-directional albedo models Simulation of CLARREO measurements from IPCC model results, including: - Calculations for pre-industrial conditions - Calculations for transient climate change with all forcings Perform parallel calculations for all-sky and clear-sky conditions Estimation of natural (unforced) variability in the simulated CLARREO data

27. Second Six Months Objective: Detection and estimation of radiative forcings Simulation of CLARREO measurements from IPCC model results, including: - Calculations for transient climate change from single forcings Calculation of spectral signatures of shortwave and longwave forcings from reference radiative transfer calculations with Modtran 4 Estimation of radiative climate forcing from simulated clear-sky CLARREO data - Projection global CLARREO simulations onto single-forcing spectral signatures to isolate time-dependent forcings - Comparison of estimates with actual forcing of the climate models - Derivation of signal-to-noise ratio using unforced variability in simulated clear-sky radiances as the noise - Characterize improvements, if any, in estimates and time-to-detection relative to existing satellite instruments Repeat forcing estimation for all-sky fluxes - Quantify degradation in forcing estimates and time-to-detection from the substitution of all-sky for clear-sky observations

28. Final Six Months Objective: Detection and estimation of radiative feedbacks Estimation of radiative climate feedbacks from the simulated CLARREO data - Estimation of surface-albedo feedbacks for clear and all-sky data - Estimation of water-vapor/lapse-rate feedbacks for clear and all-sky data - Estimation of cloud feedbacks from all-sky data only - Comparison of estimates with feedback estimates derived independently Characterize improvements in estimates and time-to-detection relative to existing satellite instruments

29. Key questions for Climate OSSEs Can the forcings from aerosols and land-use change and the feedbacks from snow and ice be detected and quantified using CLARREO data? Can the indirect shortwave forcings from aerosol-cloud interactions and the feedbacks from clouds be detected and quantified using CLARREO data? What are the implications of pixel size for the detection and quantification of forcings and feedbacks in clear-sky versus all-sky observations? To what extent is it possible to isolate forcings and feedbacks associated with changes in specific species and processes in the CLARREO measurements? Can changes in and longwave feedbacks from low, middle, high clouds be detected and quantified using the CLARREO infrared data?