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

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 uncertainty in 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 1

Satellite Products 2 Radiation CERES EBAF TOA radiation budgets TOA cloud radiative forcing (CRF) Clouds CERES MODIS SYN1degree Total Column Cloud Fraction CCCM (CloudSat, CALIPSO, CERES, MODIS) Vertically integrated Cloud Fraction Vertical Velocities (omega) MERRA Reanalysis 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

Study Groundwork 28 uncoupled - AMIP (atmosphere-only) models Climatologically prescribed SSTs 03/2000 – 02/2008 (8 years) SML: 70 – 30 South Ocean 3

Cloud Fraction (CF) Comparison 4 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

Cloud Water Path (Ice + Liquid) 5 Observations [190.3 gm − 2 ] Multimodel Ensemble [134.5 gm − 2 ] Bias [ − 55.8 gm − 2 ] A fair proxy for cloud optical depth Model simulated cloud water path is largely under estimated in the SMLs compared to CERES-MODIS observation

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) 6 At 850 hPa Multimodel Mean: 24.5% CCCM: 43.5% Bias: -19.0% *Only 23 simulations available

Vertical Velocities 7 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) Convective cloud types are commonly parameterized by the consideration of mass flux and vertical velocities while stratiform-type cloud schemes are based upon RH relationships *Only 26 simulations available Up Down

Vertical Velocities at 850 hPa 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 Down Up Down Up 8

Cloud Fraction at ~850 hPa 9 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

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? 10

11 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 TOA Reflected SW and OLR Flux differences (Model – CERES)

12 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 TOA SW and LW CRF differences (Model – CERES) CRF = All - Clr

13 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

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! 14

Questions 15

Backup 16

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Relative Humidity % 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

Summary VariableObserved Mean*Ensemble MeanMean Bias** Cloud Fraction ± 8.0 − 12.2 Cloud Water Path ± 47.0 − 55.8 TOA Reflected SW ± 8.1 − 1.7 TOA Outgoing LW ± 3.9 − 1.3 TOA SW CRF − 63.1 − 60.8 ± 8.9 − 2.3 TOA LW CRF ± 5.2 − 1.9 TOA Net CRF − 34.2 − 33.8 ± 5.8 − *Observed values are from CERES MODIS/EBAF ** Mean biases in CRFs correspond to the relative warming/cooling effects