The Relation Between SST, Clouds, Precipitation and Wave Structures Across the Equatorial

Pacific

Anita D. Rapp and Chris Kummerow

14 July 2008

AMSR Science Team Meeting

Motivation• Recent studies have

shown that warm rain systems in the Tropics may be sensitive to the underlying SST

• There have been many studies tying cloud feedbacks to SST, but it is also important to understand the relationship between the clouds and precipitation with SST

Lau and Wu (2003)

Why are warm rain systems important to the climate system?

• Warm rain systems are prevalent in the Tropics and contribute to the total tropical rainfall

• They affect the radiation budget, mostly through their interaction with shortwave radiation– In the absence of other changes, a reduction in warm rain cloud

amount would allow more solar radiation to reach the surface

• Also affect the hydrologic cycle through their moistening of the lower troposphere which preconditions the atmosphere for deep convection – A reduction in the amount of evaporating warm rain clouds could

alter the recycling timescales for deep convection

Are these systems responding to SST?Original idea was to look at retrievals from a variety of sensors and algorithms to see how warm rain systems respond to SST.

No rain Rain

What to do?Modify microwave optimal estimation algorithm to -

1. Ingest deconvolved microwave Tbs

2. Take into account partially cloud-filled pixels

3. Calculate emission/scattering for raining pixels

30° S – 30° N, 130° E – 170° W

Data -

TMI brightness temperatures

–10,19, 37, 21, 85 GHz Tbs

VIRS cloud fraction

PR precipitable water and rain height

RSS TMI-derived SST

TMI Deconvolution

Sharpenedcoast lines

Enhanced cloudscale features

• Used to place all microwave channels on a common spatial resolution

• Method utilizes footprint overlap between adjacent pixels

• Modifies footprint resolution while minimizing error propagation

Establish Convergence CriteriaRetrieval

(Determine Validity of Retrieval)

Ingest Observed Brightness Temperatures (TBs)Prior Knowledge of Atmospheric State (Water Vapor, Wind, Cloud Water)

Adjust Water Vapor, Wind, Cloud Water

(TBOBS TBSIM )T STb 1(TBOBS TBSIM ) (x x

A)T SA

1(x xA)

Minimize Cost Function:

Iterate

Basic OE Algorithm

Simulate TBs

(TBOBS TBSIM )T STb 1(TBOBS TBSIM ) (x x

A)T SA

1(x xA)

Modifications to OE Algorithm

New inputs - • Ingest deconvolved TMI Tbs• VIRS cloud fraction calculated within TMI footprint• Specify the rain column from PR-derived rain water and rain height

CLDCLRSIM aTBTBaTB )1(

TBCLD includes scattering and extinction from calculated from PR rain water

VIRS Cloud fraction within TMI footprint

Modify cost function -

Results

Sensitivity to Resampling

Sensitivity to Cloud Fraction

Sensitivity to Rain Water

Results w/ SST

No rain Rain

• One of the most obvious deep convective events in the Tropics is the Madden-Julian Oscillation

• MJO identified by numerous studies in April – May 2001

Testing Influence on Deep Convective Time Scales

• Study time series of surface, atmospheric and cloud properties

• Examine the influence of properties of warm rain systems on lower tropospheric moistening

MJO Case Study Results

Further Testing Influence on Tropospheric Water Vapor

From Serra et al. (2006)From Serra & Houze (2002)

•Examine easterly waves in the Pacific

•Determine whether the properties of warm rain clouds are correlated with tropospheric moistening

Tropical Disturbance Results

Summary•When effects of resolution, partially cloudy pixels, and rainfall are

taken into account, visible and microwave sensor retrievals show better agreement

•Results indicate a change in the behavior of cloud properties from non-raining to raining clouds

LWP changes little with SST for non-raining clouds, but strongly decreases with SST in warm rain clouds

•Microwave retrieval error diagnostics indicate that deconvolution and cloud fraction information generally improves the retrieval, but very low cloud fraction and rainfall information can influence it in either direction

•MJO case study suggests that warm rain systems are correlated to moistening in low - mid levels

•Indicates that behavior of warm rain systems may be tied to recycling timescales of deep convection

Summary and Discussion

•Easterly wave composites show that properties of non-raining clouds are fairly invariant with passage of wave

•Easterly wave composites also show that cloud properties associated with warm raining systems are correlated with moistening

•Need to study more equatorial wave cases to try to cement link between properties of warm rain clouds and lower tropospheric moistening

•Techniques used in this study could be applied to AMSR-E by combining it with other instruments in the A-Train by using information from MODIS and/or AIRS, CloudSat, and Calipso