1. Paths Forward for Hyperspectral Observational Simulation of the CMIP5 and CMIP6 Archives in Support of CLARREO Daniel Feldman, William Collins, John Paige CLARREO 2014 Fall Science Team Meeting October 28, 2014 National Institute of Aerospace 1

2. Presentation Outline Research highlights –PNAS, GMDD, and CO 2 papers Summary of May, 2014 NASA LaRC meeting OSSE development summary: –New interface with CMIP5 data –Single-threaded radiative transfer parallelization –Monte Carlo parameter-space sampling Spectral Climate Signals Proposal Future directions for CMIP6 and DS White Paper 2

3. Research Highlight: Far-IR Emissivity Calculations show frozen surfaces have substantially higher far-IR surface ε than unfrozen surfaces. Potential for positive feedback: lower far-IR ε leads to lower cooling-to-space for arid conditions. CLARREO IR spectrometer highlighted to characterize far-IR ε after cloud-clearing. Paper under embargo at PNAS until 11/3/2014. Press releases planned. Possible weekly highlight for NASA HQ. 3

4.  Strong H 2 O rotational lines in far-IR, impacting transmission and cooling.  Under dehydrated conditions, such as high altitudes and latitudes, far-IR becomes more transparent between 250 and 600 cm -1. Far-IR Transparency at Low PWV Turner et al, BAMS, 2010

5.  Climate models, satellite retrievals show low PWV at high latitudes, altitudes.  Over Greenland, Tibetan Plateau, Antarctica, clear- and all-sky OLR changes by 1-2 W/m 2 for a spectrally-gray far-IR emissivity perturbation of 0.05.  Emissivity less than 1 implies warming. Scene-Dependent Sensitivity 1 3 0.3 5 0.5 0.1 0.03 0.01

6.  Based on radiative transfer models & published indices of refraction (but NOT comprehensive observations), calculations of hemispherically- averaged far-IR surface emissivity indicate values much lower than 1.000.  Ocean & desert far-IR emissivities are low, vegetation & snow are high.  Difference between ocean and ice implies tendency to reinforce sea-ice loss in polar winter.  Implications for the radiation budget over Tibetan Plateau with dust-contaminated snow. Spectral Emissivity Calculations Ocean: Hale and Querry (1973) refractive indices of liquid H 2 O, Fresnel equation calculation. Vegetation: Extrapolation from ASTER spectral library. Desert: Glotch et al, (2007) refractive indices of common surficial minerals, Fresnel equation calculation. Snow: Warren and Brandt (2008) with correction (Mishchenko, 1994), Hapke (1993) model calculation.

7. CESM Sensitivity to Surface Emissivity  Control: CESM RCP8.5; Experiment: CESM RCP8.5 with ε modification.  After 25 years’ of integration, experiment has significantly less frozen surface coverage, warmer surface temperature, and different OLR and cooling rate patterns. Lower T s in Exp. Higher T s in Exp. Shading Indicates Significant Differences at p<0.05 Lower OLR in Exp. Higher OLR in Exp. Less Frozen Extent in Exp. More Frozen Extent in Exp. Lower Trop Heating in Exp. Higher Trop. Heating in Exp.

8. Research Highlight: Pan-Spectral OSSE Summary of pan-spectral (SW+LW) OSSE formulation. Comparison of trends from low (MIROC5) and high (HadGEM2-ES) sensitivity models. Demonstration of capability for analysis of CMIP5 and CMIP6. Paper In Revision at GMDD. 8

9. Research Highlight: CO 2 Spectroscopy Multi-institutional effort arose in response to Happer’s paper (doi:10.1142/S0217751X14600033) claiming flaws in climate model spectroscopy.10.1142/S0217751X14600033 Presentation at APS Annual Meeting in March, 2014. Two-week intensive with Marty Mlynczak in September made substantial progress towards a paper in preparation (see Marty’s talk for more details). 9

10. May, 2014 Meeting Summary High-level discussion on CLARREO’s interface with the modeling community. Importance of engaging several key climate modelers at UKMO and GFDL. Identified need to engage modelers by showing value of data for constraining model sensitivity. – e.g., clear observational signatures separating low- and high-sensitivity models. – Both past observations and future needs should be considered. – Run OSSE on multiple models for years 70-80 of 1%/yr CO2, look for pan-spectral fingerprints of cloud feedbacks. 10

11. OSSE Development Summary Pursuant to the May, 2014 meeting, several development efforts undertaken. New CMIP5 interface for OSSE. – Data formatted and gridded to serve to OSSE in homogeneous format. – Working on 10 CMIP5 models currently, radiometric validation required. bcc-csm1-1, BNU-ESM, CanESM2, CCSM4, CESM1-CAM5, FIO-ESM, HadGEM2-A0, MIROC5, MPI-ESM-LR, NorESM1-M. Single-threaded parallelization. – Implemented mechanism that farms single-threaded programs to arbitrary numbers of supercomputing nodes. – Dramatically reduce wall-clock time for stand-alone executable versions of LBLRTM, Modtran, and PCRTM. Limitation is the queue and the file system. Monte Carlo parameter-space sampling – Determine global or regional averages of SW and LW spectra with fewer RT calls by sampling the atmospheric thermodynamic and condensate state parameter space. 11

12. Paths Forward for CMIP5 and CMIP6 CLARREO OSSEs The Berkeley Team views OSSEs as a critical bridge between modeling and observation communities. Better forcing diagnostics for CMIP6 will enable clear separation of models by feedback strengths. Therefore, two goals for future OSSE work – Convince modelers of the value of observations. – Show modelers the strengths and weaknesses of existing records. 12

13. Gearing Up for CMIP6 The experiments for CMIP6 are being finalized by the CMIP panel. There will be a core (aka DECK) set of simulations and a wide range of optional experiments. Simulation phase from 2015-2020. Critical need to archive fields necessary for RF & complete RT. Hyperspectral simulator in COSP? OSSEs could expand on Obs4MIPs for CMIP6 Meehl et al, 2014 13

14. Spectral Climate Signals Proposal “Using OSSEs to Compare Climate Performance of Operational Retrieval Algorithms and Spectral Fingerprints.” – i.e., What is the value of multi-decadal-length retrieval products from AIRS/IASI/SCIAMACHY for confronting climate models? Determine, with simulations, if products derived hyperspectral retrieval algorithms can confront climate models. – Focus on T, H2O, cloud fraction, cloud OD, and cloud top temp. Introduce spurious long-term trends in retrieval algorithm constraints, run OSSE, look for spurious trends and change detection in products retrieved from OSSE. – Does fingerprint analysis fare better? Investigation will determine the value of existing records and the benefit of CLARREO measurements. Collaborative effort between LBNL, JPL, and NASA LaRC. 14

15. Aerosols Climate Model Integration Climate Model Integration AIRS/MODIS/ SCIAMACHY Instrument Emulators AIRS/MODIS/ SCIAMACHY Instrument Emulators Synthetic measurements Absorbing gases Clouds Ancillary info Snow/Sea-ice fraction Instantanteous Retrievals Radiance/Reflectanc e Trends Solar source function Retrieved Variable Trends Temperature Underlying Climate Model Trends Spectral Fingerprint Trends 15

16. Conclusions The effort at LBNL has continued to publish on the pan- spectral OSSE capability. New spin-off results have also shown the scientific value of CLARREO-like instruments. The OSSE development effort has been configured for CMOR-ized CMIP5 model output. Pan-spectral OSSE can now take advantage of stand-alone radiation codes through task-farming. We plan to engage the modeling community through a demonstration of how spectra can constrain model sensitivity. We plan to investigate the susceptibility of retrieval algorithms to long-term biases using OSSEs. 16