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, WAWorkshop 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

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Satellite Products

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Radiation• CERES EBAF

TOA radiation budgets

TOA cloud radiative forcing (CRF)

Clouds• CERES MODIS

SYN1degreeTotal Column Cloud Fraction

• CCCM (CloudSat, CALIPSO, CERES, MODIS)Vertically integrated Cloud FractionVertical 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

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Cloud Fraction (CF) Comparison

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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)

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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)

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At 850 hPaMultimodel Mean: 24.5%CCCM: 43.5%

Bias: -19.0% *Only 23 simulations available

Vertical Velocities

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At 850 hPaMERRA: 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

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Cloud Fraction at ~850 hPa

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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?

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Modeled TOA reflected SW flux is higher while OLR is lower than CERES observations over the SMLsThese results do not make physical sense compared to underestimated CF and CWP in model

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

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The magnitude of TOA SW (LW) CRF cooling (warming) is underestimated in the SMLsRegions of positive (negative) biases are consistent with the SW (LW) radiation flux results

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

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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 modelsSummary 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!

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Questions

erica.dolinar@my.und.edu

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Backup

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RelativeHumidity

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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

SummaryVariable Observed

Mean*Ensemble

MeanMean Bias**

Cloud Fraction 81.5 69.3 ± 8.0 −12.2 Cloud Water Path

190.3 134.5 ± 47.0 −55.8

TOA Reflected SW

105.3 103.6 ± 8.1 −1.7

TOA Outgoing LW

223.8 222.5 ± 3.9 −1.3

TOA SW CRF −63.1 −60.8 ± 8.9 −2.3 TOA LW CRF 28.9 27.0 ± 5.2 −1.9 TOA Net CRF −34.2 −33.8 ± 5.8 −0.4

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