III. Science Questions: Climate Prediction and Climate Model Testing 1:30 – 3:00Forcing, Sensitivity, and FeedbacksBill Collins How CLARREO AppliesStephen Leroy and Michael Mishchenko 3:00 – 3:15Break 3:15 – 4:45Climate TrendsV. Ramaswamy How CLARREO AppliesPeter Pilewskie Joao Teixeira Kevin Bowman 4:45 – 5:00Discussion

III. Science Questions: Climate Prediction and Climate Model Testing Peter Pilewskie: CLARREO Visible and Near-Infrared Studies Stephen Leroy: Testing Climate Models with CLARREO: Feedbacks and Equilibrium Sensitivity V. Ramaswamy: Radiation spectra at TOA and climate diagnoses Mike Mishchenko: Constraining climate models with visible polarized radiances Kevin Bowman: Observational constraints on climate feedbacks: A pan-spectral approach

Climate Model Observing System Simulation Experiments Bill Collins UC Berkeley and LBL with A. Lacis (GISS) and V. Ramaswamy (GFDL) and J. Chowdhary, D. Feldman, S. Friedenrich, L. Liu, V. Oinas, D. Schwarzkopf

Simulation and the CLARREO questions Societal objective of the development of an operational climate forecast: The critical need for climate forecasts that are tested and trusted through … state-of-the-art observations. Objectives of OSSE: Use models as “perfect worlds” to understand utility of CLARREO for detection and attribution vs. models. Prepare climate modeling community for direct application of all-sky radiances for evaluation and assimilation.

Climate prediction and its components IPCC AR4, 2007

Historical radiative forcing IPCC AR4, 2007 Probability that historical forcing > 0 is very likely (90%+). However, confidence in short-lived agents is still low at best.

Forcing scenarios for 21 st century Longwave: The 5 to 95 percentile range of at 2100 is ~50% of the mean. Shortwave: The models do not agree on sign or magnitude of forcing. IPCC AR4, 2007

Projection of regional temperatures IPCC AR4, 2007 Roughly 2/3 of warming by 2030 is from historical changes. Uncertainties at 2100 are from physics and emissions.

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 to Change in cloud radiative effects in 21st century: A1B Scenario IPCC AR4, 2007

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

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?

Application of CLARREO to Models Forcing Climate System CLARREO Attributed Forcing Forcing Climate System CLARREO Attributed Feedback Forcing Projection Feedback Projection Climate Models

Schematic of Tests Forcing Climate Models CLARREO Emulator CLARREO Emulator Simulated Forcing Compare Forcing Climate Models CLARREO Emulator CLARREO Emulator Simulated Feedback Compare Model Feedback Forcing Projection Feedback Projection

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

Individual feedbacks in Climate Models IPCC AR4, 2007

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

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).

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

Candidate CLARREO Emulators 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: AER, GISS, GFDL, and NCAR LBL codes Berk et al, 1999

TOA shortwave spectrum Profile: AFGL mid-latitude summer with 2000 AD long-lived greenhouse gases. Sun-satellite geometry: solar zenith angle = 53 o, satellite zenith = 0 o. Spectral parameters: 15 cm -1 resolution with no instrumental convolution. Radiative transfer code: Modtran 4,

Shortwave spectral forcings Absolute ForcingRelative Forcing Forcing calculations: CO 2 : 287 to 574 ppmv (2×CO ) N 2 O: 275 to 316 ppbv ( ) CH 4 : 806 to 1760 ppbv ( ) N 2 O: 100% to 120% PW(2×CO 2 feedback)

Primary steps in the OSSE Phases for the study: Linking the CLARREO emulator 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

Natural variability in the spectra Huang et al, 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

Issues for the Emulation For speed and expediency, we recommend using using the existing IPCC archives 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 grid point. Alternate, but remote, possibility: “time-slice” experiments Advantage: interactive coupling and capture space-time sampling Time Slice

Additional Issues for the Emulation 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

First stage of the OSSE Objective: Configuration and initiation of the OSSEs 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

Second stage of the OSSE 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 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 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

Conclusion of the OSSE 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

Key questions for Climate OSSEs Can clear-sky shortwave forcings and feedbacks be detected and quantified using CLARREO data? Can all-sky shortwave forcings and feedbacks be detected and quantified using CLARREO data? Can all-sky longwave forcings and feedbacks be detected and quantified using CLARREO data? To what extent is it possible to isolate forcings and feedbacks associated with changes in specific species and processes in the CLARREO measurements?