Erica K. Dolinar Xiquan Dong and Baike Xi University of North Dakota

1. Evaluation of CMIP5 Simulated Clouds and TOA Radiation Budgets in the SMLs Using NASA Satellite Observations Erica K. Dolinar Xiquan Dong and Baike Xi University of North Dakota This talk is based on Dolinar et al. (2014, Clim. Dyn.) March 18, 2014 | University of Washington, Seattle, WA Workshop on Clouds, Radiation, Aerosols, and the Air-Sea Interface in the S. Midlatitude Ocean

2. Motivation • “In many climate models, details in the representation of clouds can substantially affect the model estimates of cloud feedback and climate sensitivity. Moreover, the spread of climate sensitivity estimates among current models arises primarily from inter-model differences in cloud feedbacks. Therefore, cloud feedbacks remain the largest source of uncertaintyin climate sensitivity estimates.” – IPCC Fourth Assessment Report (2007) • Want to understand the impacts of simulated clouds on the TOA radiation budget and cloud radiative forcings in our current climate so that we may better predict the future climate

3. Satellite Products • Clouds • CERES MODIS SYN1degree • Total Column Cloud Fraction • CCCM (CloudSat, CALIPSO, CERES, MODIS) • Vertically integrated Cloud Fraction • Radiation • CERES EBAF • TOA radiation budgets • TOA cloud radiative forcing (CRF) Products are Level-3 and have been either downloaded or provided by Science Team members *Caveat Observations have uncertainties (Dolinar et al. 2014) but are used as “truth” in this study • Vertical Velocities (omega) • MERRA Reanalysis

4. Study Groundwork • 28 uncoupled - AMIP (atmosphere-only) models • Climatologically prescribed SSTs • 03/2000 – 02/2008 (8 years) • SML: 70 – 30 South Ocean

5. Cloud Fraction (CF) Comparison Observations [81.5%] Multimodel Ensemble [69.3%] Bias [−12.2%] Model simulated total cloud fraction is largely under estimated over the SMLs compared to CERES-MODIS observed CF

6. A fair proxy for cloud optical depth Cloud Water Path (Ice + Liquid) Observations [190.3 gm−2] Multimodel Ensemble [134.5 gm−2] Bias [−55.8 gm−2] Model simulated cloud water path is largely under estimated in the SMLs compared to CERES-MODIS observation

7. CF Profile The under estimation of CF in the SML oceans is primarily a result of under estimated low- and mid-level (950 – 500 hPa) clouds. There does exist some over estimation of cloud fraction at higher levels (~250 hPa) At 850 hPa Multimodel Mean: 24.5% CCCM: 43.5% Bias: -19.0% *Only 23 simulations available

8. Vertical Velocities Convective cloud types are commonly parameterized by the consideration of mass flux and vertical velocities while stratiform-type cloud schemes are based upon RHrelationships At 850 hPa MERRA: 1.0 hPa day-1 (down) Multimodel Mean: -0.1 hPa day-1 Regime shift… The dynamic forcing in this region is different (or slightly modified) than what is observed (reanalyzed) UpDown *Only 26 simulations available

9. Vertical Velocities at 850 hPa Down Up Down Up The overall distribution of vertical velocities (convection/subsidence) at 850 hPa is correctly simulated by the multimodel ensemble in the Southern Mid-latitudes, but either the strength of the descending branch of the Hadley Cell is weaker or the ascending branch of the Ferrell Cell is stronger than reanalyzed ones

10. Cloud Fraction at ~850 hPa Observations [43.5%] Multimodel Ensemble [24.5%] Bias [−19.0%] The largest biases at ~850 hPa coincide with the ascending/descending branches of the Hadley and Ferrell Cells

11. Summary I: CF Comparisons • Total column cloud fraction is under estimated, on average, by the 28 model ensemble by 12.2% in the Southern mid-latitudes over the ocean • Cloud water path is under estimated by 55.8 gm−2 • Currently large uncertainties in observed CWC profiles • Cloud fraction is under estimated by ~20% in the low-levels (~850 hPa) (23/28 models) • Due to, but not limited to, a potential dynamical regime shift or lack of cloud water • Would be interesting to analyze other simulated synoptic conditions • What effect do these results have on the TOA radiation budget?

12. TOA Reflected SW and OLR Flux differences (Model – CERES) Modeled TOA reflected SW flux is higher while OLR is lower than CERES observations over the SMLs These results do not make physical sense compared to underestimated CF and CWP in model

13. TOA SW and LW CRF differences (Model – CERES) CRF = All - Clr The magnitude of TOA SW (LW) CRF cooling (warming) is underestimated in the SMLs Regions of positive (negative) biases are consistent with the SW (LW) radiation flux results

14. The simulated magnitude of the Net CRF cooling is under estimated in the SMLs but there does exist an area of stronger cooling due to clouds between S. America and Australia in the models • Summary II: TOA Radiation Results • All TOA radiation and the cloud radiative heating/cooling is under estimated in the SMLs • Areas of over estimated SW/Net cooling due to clouds • Results are consistent with each other but not with corresponding CF and CWP results • Less clouds, more reflection/cooling and less outgoing/warming? How? • A topic for further consideration and research

15. Acknowledgements • Workshop organizers • Drs Jonathan Jiang and Hui Su at JPL for their help and support over the past year • Research group at UND • All of you!

16. Questions erica.dolinar@my.und.edu

17. Backup

19. RelativeHumidity 15-20% uncertainty in AIRS RH Stratiform type clouds are commonly parameterized with the consideration of relative humidity Relative humidity is over estimated at all levels (with the exception of one model below 900 hPa) BUT… we do not know which models contain both liquid and ice RHs so we will not put any faith in these results *Only 13 simulations available

20. Summary *Observed values are from CERES MODIS/EBAF ** Mean biases in CRFs correspond to the relative warming/cooling effects