investigation of earth radiation budget variability by cloud object analysis

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Investigation of Earth radiation budget variability by cloud object analysis Seiji Kato 1 , Kuan-Man Xu 1 , Takmeng Wong 1 , Patrick C. Taylor 1 , Tristan S. L’Ecuyer 2 , Shengtao Dong 3 , Jenny Chen 3 , Sunny Sun-Mack 3 , Fred Rose 3 , Walter Miller 3 , and Yan Chen 3 1 NASA Langley Research Center 2 University of Wisconsin 3 Science System & Applications Inc.

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Investigation of Earth radiation budget variability by cloud object analysis. Seiji Kato 1 , Kuan-Man Xu 1 , Takmeng Wong 1 , Patrick C. Taylor 1 , Tristan S. L’Ecuyer 2 , Shengtao Dong 3 , Jenny Chen 3 , Sunny Sun-Mack 3 , Fred Rose 3 , Walter Miller 3 , and Yan Chen 3 - PowerPoint PPT Presentation

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Investigation of Earth radiation budget variability by cloud object

analysisSeiji Kato1, Kuan-Man Xu1, Takmeng Wong1, Patrick C. Taylor1,

Tristan S. L’Ecuyer2, Shengtao Dong3, Jenny Chen3, Sunny Sun-Mack3, Fred Rose3, Walter Miller3, and Yan Chen3

1NASA Langley Research Center2University of Wisconsin

3Science System & Applications Inc.

CCCM product• Contains: 1. Merged CALIPSO, CloudSat derived clouds, CERES TOA radiative flux (SW, LW,

and WN), MODIS (CERES_ST) derived cloud properties both along CALIPSO-CloudSat ground-track and over the whole CERES footprint,

2. MODIS derived cloud properties by an enhanced cloud algorithm, 3. CALIPSO and MODIS derived aerosol properties4. Vertical radiative flux profiles computed with CALIPSO, CloudSat, and MODIS

derived cloud properties.• 57 months of data (July 2006 through April 2011) are available from

http://eosweb.larc.nasa.gov/PRODOCS/ceres-news/table_ceres-news.html

Objectives and Scientific questions

• Understand radiation budget variability caused by clouds

• Reduces the uncertainty of cloud object analysis by CALIPSO and CloudSat observations

• How is the frequency of occurrence of cloud objects perturbation related to the variability of TOA radiation budget?

• How do cloud properties within cloud objects change with dynamical state or sea surface temperature?

A cloud object is a contiguous patch of cloudy regions with a single dominant cloud-system type, shifting from Eulerian to Lagrangian views of cloud systems

The shape and size of a cloud object is determined by the satellite footprint data and by the footprint selection criteria for a given cloud-system type

Type of cloud objects Cloud top height Cloud fraction

Latitude band

Stratus (St) < 3 km 99 - 100% 40S – 40N

Stratocumulus (Sc) < 3 km 40 - 99% 40S – 40N

Cumulus (Cu) < 3 km 10 - 40% 40S – 40N

Cloud objects

Extended cloud object type

Cloud Objects Numbers (CCCM Matched)

March 2008 (a) 100-150 Km (b) 150-300 Km (c) 300 Km and up

Deep Conv. 763 (47) 791 (131) 457 (250)

SC 1 12321 (438) 3533 (512) 911 (557)

SC 2 8596 (331) 2683 (369) 829 (490)

SC 3 4118 (180) 1287 (262) 542 (328)

CC1 371 (14) 7 (0) 0 (n/a)

CC2 1592 (87) 190 (56) 14 (9)

CC3 4127 (181) 1637 (310) 504 (342)

1: cloud fraction 0.1 to 0.4(Trade/shallow cumulus)2: cloud fraction 0.4 to 0.99 (Transition stratocumulus)3: cloud fraction great than 0.99 (Solid stratus)a: size of 100 km to 150 kmb: 150 to 300 km c: great than 300 km.

TOA net radiative flux variability

Kato 2009 J. Climate

Cloud macroscopic property difference due to Positive MEI versus negative MEI

• Separate cloud properties derived from MODIS or CALIPSO/CloudSat by MEI index

• Cloud type: SC3c• Positive MEI: 200608 (0.759), 200609 (0.793),

200610 (0.892), 200611 (1.292)• Negative MEI: 200808 (-0.266), 200809 ( -

0.643), 200810 (-0.78) 200811 (-0.621)

Cloud fraction derived from CALIPSO and CloudSat

Positive MEI Negative MEI

Cloud top height derived from CALIPSO and CloudSat

Positive MEI Negative MEI

km km

Cloud base height derived from CALIPSO and CloudSat

km km

Positive MEI Negative MEI

Cloud top comparisonCALIPSO/CloudSat

Red: Positive MEIBlue: Negative MEI

Pro

port

iona

l to

coun

ts

Precipitationdifference of the fraction of precipitating clouds

Positive - Negative

Initial task timeframe (tentative)

• January-May 2013: Cloud object subletting from SSF and CCCM

• April-July/2013: Initial analysis of cloud objects, comparison with previous results (Xu et al. 2007, 2006, 2007, 2008)

• Aug. – Dec./2013: Initial analysis of active sensor derived cloud properties

• Jan-Sep/2014: Understanding relationship between cloud objects and radiation variability.

Cloud profile difference

Uppermost cloud top height (km)

Hei

ght (

km)

Positive – Negative MEI

T and RH difference (positive – negative MEI)

Uppermost cloud top height (km)

Hei

ght (

km)

Reflected from Cloudy Regions

-79

Reflected by Atmosphere

Reflected from Clear

Regions-21

Reflected at Surface

-24

Absorbed at Surface

163

Absorbed by

Atmosphere77

Sensible Heat-21

Latent Heat-88

Emitted from Clear

Regions-104

Surface Emission

-398

Absorbed at Surface

344

-136

Emitted from Cloudy Regions

221

Atmosphere LW cooling

-186

Outgoing LW Radiation

-240

Incoming Solar Radiation

340

Reflected Solar

Radiation-100

Surface Imbalance 0.5

TOA Imbalance 0.5

Radiative Effect of Clouds SW LW NETTOA -47 26 -21ATM 4 -4 0SFC -51 30 -21

Emitted from Clear

Regions123

Earth’s Energy Budget