Pan-spectral Observational Signals from Climate Models with Different Ice- and Low-Cloud Feedbacks

1. Pan-spectral Observational Signals from Climate Models with Different Ice- and Low-Cloud Feedbacks Daniel Feldman1 Bill Collins1,2, Daniel Coleman1April 11, 2012 1 Lawrence Berkeley National Laboratory, Earth Sciences Division 2 University of California-Berkeley, Department of Earth & Planetary Science

2. Outline • Recap of tasks proposed for the CLARREO SDT. • Pan-spectral development update. • Simulations of different cloud feedback strengths. • Discussion.

3. Proposed Tasks for the CLARREO SDT • The Berkeley group has proposed to contribute the following to the CLARREO SDT: • Utilization of simulated CLARREO data to estimate change detection time in SW reflectance spectra • Production of pan-spectral (SW+IR) OSSE spectra. • Interfacing different scenarios (varying forcings and feedbacks) of CCSM3 into the CLARREO OSSE framework. • Production and analysis of spectra derived from different orbits. • Development and implementation of tools to produce OSSE spectra based on CMIP5 database.

4. Validation of LW Simulations • Upwelling TOA broadband fluxes have been validated for the clear-sky LW OSSE simulations against CAM RT. • LW spectra have been validated against CAM and RRTM for clear- and all-sky conditions. μ= 0.7 W/m2 σ = 0.2 W/m2

5. Towards Observational Constraints on Cloud Feedbacks • The OSSE framework evaluate the observational signatures of climate models with different feedback strengths. • Uncertainty in climate sensitivity linked to low-cloud feedbacks. Bony, et al, 2006

6. Multiple-Resolution Simulations: Differences in Feedbacks • We have OSSE data from several runs all forced only with CO2 increasing at 1% per year. • Simulations are at T31 (~ 3.75°), T42 (~2.8°), and T85 (1.4°) horizontal resolutions. • Feedbacks are stronger for higher spatial resolution models • Due to boundary-layer parameterizations that lead to over-prediction of low-level cloud fraction. • Sea-ice model predicts higher fractional coverage for higher resolution. Kiehl et al 2006

7. Results of Different Climate Sensitivities at 2xCO2 T31 ΔLOWCLD @2XCO2 Δ% T85 ΔLOWCLD @2XCO2 Δ% • Identical model physics except for low-cloud and ice albedo feedbacks and resolution effects. • Highest-res (T85) run has 17% higher equilibrium climate sensitivity than lowest-res (T31) run. T85 has 5% higher transient sensitivity than T31. • Differences in TS and LOWCLD in arctic and over storm tracks. T31 ΔTS @2XCO2 ΔK T85 ΔTS @2XCO2 ΔK

8. Multiple Resolution Simulations Status • Subsequently, one of the simulations (T31) will be used as a measurement proxy. • T42-T31 and T85-T31 time-series are analogous to comparing measurements with models. • With this approach, we explore measurement record length required to reject a model, where the model’s physics are consistent with the measurement proxy modulo low-cloud and sea-ice feedbacks.

9. Δ Clear-sky OLR Broadband trends in OLRC are associated with water vapor and temperature. Increases in OLRC from surface and tropospheric temperature rise in arctic. Decreases in OLRC at tropics and subtropics from water vapor due to thermo.

10. Δ All-sky OLR Broadband trends are associated with water vapor, temperature, and UT clouds. Polar amplification blunted by clouds. Increases in water vapor offset by subtropical clouds. Large decrease in OLR around ITCZ.

11. Δ Clear-sky broadband albedo Broadband trends are associated with changes in snow, and sea ice and H2O. Little change in albedo at low latitudes. Snow and ice changes are significant at poles.

12. Δ All-sky broadband albedo Broadband trends are associated with changes in clouds, snow, and sea-ice. Clouds offset loss of sea-ice in T31 run. No significant increase in low-latitude clouds in T85 run.

13. Time Series Comparison Analysis • We utilize the formulae from Weatherhead et al [1998] and described in Feldman et al [JGR, 2012] to estimate the time required to differentiate two time series. • AR(1) noise process. • Linear secular trend derived from the difference of the two time series. • Trend and noise assumed to be stationary.

14. Formula for Change Detection • The time required to say that one time-series differs from another requires estimates of : • Natural variability • Measurement uncertainty • Uncertainty in noise and trend estimation from a short time series. Scaling factor for length of record Detection time Std dev of noise process Measurement uncertainty Correlated natural variability Difference trend AR(1) of noise process

15. Clear-sky OLR differentiation T31 is measurement proxy, T42 & T85 are models that differ from that proxy. OLRC record length of 60 years required to reject T42, 50 years to reject T85. Differences first emerge at high latitudes from Ts & tropics from Q.

16. All-sky OLR differentiation OLR record length of 60 years required to reject T42 and T85. Differences first emerge in high latitudes from Ts; in tropics from Q and clouds.

17. Clear-sky albedo differentiation αclr record length of 50 years required to reject T42, 40 years to reject T85. Differences first emerge at high latitudes from changes in sea-ice fraction.

18. All-sky albedo differentiation αall record length of 45 years required to reject T42, 25 years to reject T85. Differences first emerge at many latitudes, prominently in tropics and high latitudes.

19. Comparing Trends in Albedo and OLR • For multiple resolution runs, trends in broadband quantities are similar except in the arctic (α and OLR), NH tropics (OLR), and subtropics (OLR). Lower emission Higher emission Lower reflection Higher reflection W m2 decade-1 decade-1

20. Δ Clear-sky LW spectral radiance trends Similar spectral trends from strat. cooling, differences in surface warming (window band). Cooling of stratosphere apparent in CO2 bands. Warming of surface and troposphere apparent in window band. Increases in water vapor seen in water vapor bands.

21. Δ All-sky LW spectral radiance trends Similar to clear-sky radiance trends except for signals of ITCZ in far-IR and window bands. Polar warming offset by clouds in window bands.

22. Δ Clear-sky SW spectral reflectance trends Similar spectral trends are associated with changes in snow, and sea ice, H2O, CO2. Moistening of atmosphere seen in darkening of H2O overtone bands. Decrease in snow and sea-ice seen in visible portions of the spectrum. Similar spectral shapes for all 3 simulations.

23. Δ All-sky spectral reflectance trends Differences at low latitudes in H2O overtone bands and across visible band at ITCZ. Significant differences across visible portion of SW spectrum, especially for T85 run. Dipole feature at high latitudes at 1400 and 1900 nm.

24. Discussion • The pan-spectral OSSE for CCSM3 is fully-operational. • Pan-spectral OSSE simulations indicate that certain LW window band, SW visible region and SW H2O overtone bands will be useful for differentiating models according to low-cloud feedbacks. • Differentiation time analysis for multiple resolution calculations is still forthcoming, but multi-decadal length broadband records required to understand low-cloud and sea-ice feedbacks. • Observational tests of climate model response are the ultimate goal … this may provide a systematic path for getting there.

25. Future Directions • Finish analysis for multiple resolution calculations. • Comparison between CM2 and CCSM3. • PC methods for faster change detection. • Continue laying the groundwork for OSSE simulations using the reporting framework for CMIP5. • Acknowledgements:This research was supported by NASA grants NNX08AT80G, NAS2-03144, NNX10AK27G and NNX11AE65G , and by the Ames Research Center. NASA High-End Computing grants SMD-08-0999,SMD-09-1397, SMD-10-1799 and provided computational resources produce the simulations. Thanks to Don Anderson, Gail Anderson, Vincent Ross, Lex Berk, David Young, Bruce Wielicki, Zhonghai Jin, Rosemary Baize, Crystal Schaaf, Zhousen Wang, Michael Dreibelbis and the High-End Computing technical support team of NASA User Services.